Application of Data Mining in Crime Rate Prediction

This article discusses the concept of data mining and its types, and the necessity of using data mining technique in crime rate prediction. It also explores the advantages and disadvantages of data mining in crime rate prediction and how it helps in prediction of crime rate in UK. The article also includes a literature review, research objectives, research questions, methodology, discussion and findings, conclusion and recommendations, and limitations of the research.

Application of data
mining in crime rate
prediction

Abstract
Using a vast variety of techniques, one could use this records to increase revenues, reduce
fees, improve client relationships, lessen risks and more. Data mining is the manner of
information information via cleaning raw facts, locating styles, developing fashions, and testing
those fashions. It includes records, device learning, and database structures. data mining is an
activity of identifying patterns, correlation and anomalies within wide range of data sets in order
to predict results. Through using data mining technique, businesses can avail information for
reducing crime, increasing revenue, reducing risk and improving customer relationship. Police
department mainly use data mining tools in crime rate prediction and understand data through
cleaning raw data, creating models and finding patterns. Crime is certainly one of transcendent
and disturbing part of our preferred public. Ordinary first-rate variety of violations are
completed, those successive wrongdoings have made the existences of ordinary citizens
worrying. Thus, keeping the crime from taking place is a essential challenge. In the new time, it
is visible that automated reasoning has tested its significance in nearly all of the subject and
wrongdoing expectation is one in every of them. However, it's far expected to keep the
exceptional information set of the wrongdoing that has happened as this information may be
utilized for destiny reference. The potential to foresee the wrongdoing that may take region in
future can assist the regulation authorization offices in forestalling the wrongdoing earlier than it
takes place.

Contents
Abstract............................................................................................................................................2
INTRODUCTION...........................................................................................................................4
Research Objectives...............................................................................................................4
Research Questions................................................................................................................5
Rationale for the research.......................................................................................................5
LITERATURE REVIEW................................................................................................................5
What is the concept of data mining along with its types?......................................................5
Why it is necessary to use data mining technique in crime rate prediction?..........................6
What are the advantages and disadvantages of data mining in crime rate prediction?..........9
How data mining helps in prediction of crime rate in UK?.................................................11
Methodology..................................................................................................................................14
Discussion and Findings................................................................................................................16
Conclusion and Recommendations................................................................................................23
Limitations of the research.............................................................................................................25
REFERENCES..............................................................................................................................27

INTRODUCTION
Data mining is the method of finding anomalies, styles and correlations within massive facts
units to are expecting outcomes (Agarwal and et.al., 2020). Using a vast variety of techniques,
one could use this records to increase revenues, reduce fees, improve client relationships, lessen
risks and more. Data mining is the manner of information information via cleaning raw facts,
locating styles, developing fashions, and testing those fashions. It includes records, device
learning, and database structures. Data mining is a manner to extract know-how out of
commonly large statistics units; in other words it is an method to discover hidden relationships
amongst facts through using synthetic intelligence methods. The information which is availed
through data mining helps in reducing crime as it helps in identifying the culprits faster and also
find the areas that are affected by crime. Criminology is one of the maximum important fields for
applying information mining. It encompasses the system of coming across hidden styles and
relationships in large amounts of information. This allows for making accurate and dependable
predictions of destiny activities, primarily based on the identity and characterization of these
styles and trends in historical records. The respective research will explain the concept of data
mining along with its types, why it is necessary to use data mining technique in crime rate
prediction and the advantages and disadvantages of data mining in crime rate prediction. It is
these structures that are responsible for storing information that comes out of the smallest of
transactions into the database. So, statistics related to sale, purchase, human capital control, and
other transactions are saved in database servers by means of OLTP systems. Now, pinnacle
executives need access to statistics primarily based on statistics to base their decisions on. This is
where on line analytical processing or OLAP systems enter the picture. Data warehouses and
different OLAP systems are built an increasing number of due to this very need of or top
executives. We don’t most effective want data however additionally the analytics associated with
it to make better and more profitable selections. OLTP and OLAP structures work in tandem.
Research Objectives
 To determine the concept of data mining along with its types.
 To analyze the necessity to use data mining technique in crime rate prediction.
 To identify the advantages and disadvantages of data mining in crime rate prediction.
 To analyze how data mining helps in prediction of crime rate in UK.

Research Questions
 What is the concept of data mining along with its types?
 Why it is necessary to use data mining technique in crime rate prediction?
 What are the advantages and disadvantages of data mining in crime rate prediction?
 How data mining helps in prediction of crime rate in UK?
Rationale for the research
The primary rationale for the respective research is to enhance my overall knowledge about
the concept of application of data mining in crime rate prediction. This is because I will be able
to develop new skills as well as implement the same in any further researches that I undertake.
This will help in conducting the research effectively as well as gathering appropriate data in an
effective manner. Apart from this, another reason for choosing this research is that it is linked
with the course that I am currently undertaking. Hence, I want to ensure that I implement all the
learnings from that I gain from the respective research, I implement in the future. This is because
it will not only help me in completing the respective research in an effective manner, but also, I
will be able to ensure that the findings as well as conclusions of the research are relevant as well
as valid and reliable.
LITERATURE REVIEW
What is the concept of data mining along with its types?
According to Lin, Chen and Yu (2017), data mining is an activity of identifying patterns,
correlation and anomalies within wide range of data sets in order to predict results. Through
using data mining technique, businesses can avail information for reducing crime, increasing
revenue, reducing risk and improving customer relationship. Police department mainly use data
mining tools in crime rate prediction and understand data through cleaning raw data, creating
models and finding patterns (Alam, and et.al., 2021). Data mining mainly includes machine
learning, statistics and database system. There are various types of data mining such as
clustering, association, data cleaning, data visualization, classification, machine learning,
prediction and neural networks. It has been identified that Data mining can be utilized to show
crime detection issues. The thought here is to attempt to catch long stretches of human
experience into PC models through data mining. Crimes are a social aggravation and cost our
general public sincerely in more than one way. Any research that can help in tackling crimes

quicker will pay for itself. These types of data mining helps in managing the data, improve data
quality, make data readably understand to individuals, classify crime on the basis of crime and
also help in identifying all sorts of data patterns. Each types of data mining have its own method
and ways for managing the data.
Criminology is a method that pursuits to identify crime characteristics (Brayne, 2020).
Actually crime evaluation consists of exploring and detecting crimes and their relationships with
criminals. The excessive extent of crime datasets and additionally the complexity of relationships
between these types of records have made criminology the perfect area for making use of records
mining techniques. Identifying crime characteristics is step one for developing in addition
evaluation. The expertise this is gained from facts mining processes is a very useful device that
can help and guide police forces. Data mining is the method of coming across actionable data
from massive sets of information. Data mining makes use of mathematical analysis to derive
styles and developments that exist in records.
Why it is necessary to use data mining technique in crime rate prediction?
As per the opinion of Sangani, Sampat and Pinjarkar (2019), it is important to use data
mining techniques in crime rate prediction as it helps in solving the crime in effective and
efficient manner. Through data mining technique, the crime department can also avail productive
results when applied on crime datasets (Chamikara and et.al., 2020). The information which is
availed by data mining technique helps in producing significant outcomes from crime report
datasets. It is used in crime rate prediction for exploring and detecting crime and their
relationship with criminals. The approaches of data mining are very useful for police forces.
Process of data mining is based on algorithms to analyse and extract information due to which
the hidden pattern and relationship within the data is automatically discovers. By using data
mining technique, police department can gather crude information and separate data which can
be applied to make expectation in numerous true circumstances. The methods of data mining are
used to estimate the future wrongdoing patterns of UK. It also helps in assuming the significant
part in criminal concealment and wrongdoing control in various nations. Data mining also helps
in extracting valuable data from enormous datasets and also maintains the connection between
the traits of data.
Data mining helps in fixing the crimes quicker and this technique gives exact effects while
carried out on crime dataset, the records acquired from the facts mining strategies can assist the

police branch. Produce crucial outcomes from crime file datasets. The very step in examine of
crime is crime analysis (Fouad and et.al., 2020). Crime analysis and prediction is a scientific
approach for figuring out the crime. This machine can expect area that have high chance for
crime occurrences and visualize crime prone place. Using the concept of information mining we
are able to extract previously unknown, useful records from an unstructured statistics. Businesses
in recent times are amassing information at a completely putting rate. The sources of this big
records circulate are various. It could come from credit card transactions, publicly available
client records, data from banks and monetary establishments, as well as the statistics that
customers should provide simply to apply and down load an software on their laptops, cell
telephones, capsules, and computers. It isn't always easy to store such big quantities of statistics.
So, many relational database servers are being continuously constructed for this purpose. Online
transactional protocol or OLTP systems also are being advanced to keep all that into special
database servers. OLTP structures play a crucial role in assisting groups feature smoothly.
The Descriptive Data-Mining Tasks can also be further divided into four types that are as
follows: Clustering Analysis (Islam and et.al., 2021). Association Rules Analysis. Sequence
Discovery Analysis. Data mining is the process of searching large sets of data to look out for
patterns and trends that can't be found using simple analysis techniques. Data mining has several
types, including pictorial data mining, text mining, social media mining, web mining, and audio
and video mining amongst others. Data mining is the process of discovering actionable
information from large sets of data. Data mining uses mathematical analysis to derive patterns
and trends that exist in data. Businesses these days are collecting data at a very striking rate. The
sources of this enormous data stream are varied. It could come from credit card transactions,
publicly available customer data, data from banks and financial institutions, as well as the data
that users have to provide just to use and download an application on their laptops, mobile
phones, tablets, and desktops (Marmo, 2021). It is not easy to store such massive amounts of
data. So, many relational database servers are being continuously built for this purpose. Online
transactional protocol or OLTP systems are also being developed to store all that into different
database servers. OLTP systems play a vital role in helping businesses function smoothly. Data
mining is the method of discovering actionable records from large sets of records. Data mining
uses mathematical evaluation to derive styles and tendencies that exist in information.
Businesses these days are amassing statistics at a completely putting rate. The sources of this

good sized data stream are numerous. It ought to come from credit card transactions, publicly
available client facts, records from banks and financial institutions, as well as the statistics that
customers need to provide just to apply and down load an utility on their laptops, cellular phones,
capsules, and desktops.
The facts which is availed via data mining approach enables in producing substantial
effects from crime document datasets. It is used in crime fee prediction for exploring and
detecting crime and their courting with criminals. The tactics of statistics mining are very useful
for police forces. Process of statistics mining is based totally on algorithms to analyse and extract
records due to which the hidden sample and dating within the information is routinely discovers.
By the use of information mining method, police branch can gather crude records and separate
information which can be carried out to make expectation in numerous proper occasions. The
methods of statistics mining are used to estimate the future wrongdoing styles of UK. It
additionally allows in assuming the sizable element in criminal concealment and wrongdoing
control in numerous nations. Data mining also helps in extracting precious information from
significant datasets and also continues the relationship among the trends of facts.
It is these systems that are responsible for storing data that comes out of the smallest of
transactions into the database. So, data related to sale, purchase, human capital management, and
other transactions are stored in database servers by OLTP systems. Now, top executives need
access to facts based on data to base their decisions on. This is where online analytical
processing or OLAP systems enter the picture. Data warehouses and other OLAP systems are
built more and more because of this very need of or top executives. We don’t only need data but
also the analytics associated with it to make better and more profitable decisions. OLTP and
OLAP systems work in tandem.
Data mining is the process of coming across actionable facts from big sets of facts. Data
mining uses mathematical evaluation to derive patterns and tendencies that exist in information.
Typically, those styles can not be discovered by using traditional information exploration
because the relationships are too complex or due to the fact there is too much statistics. The idea
of data mining has been with us seeing that lengthy before the digital age. The idea of making
use of records to information discovery has been round for centuries, beginning with guide
formulation for statistical modeling and regression analysis. In the Nineteen Thirties, Alan
Turing delivered the idea of a normal computing system that might carry out complicated

computations. This marked the rise of the electromechanical laptop and with it, the ever-
increasing explosion of virtual information that keeps to this very day. It is these structures that
are responsible for storing information that comes out of the smallest of transactions into the
database. So, statistics related to sale, purchase, human capital control, and other transactions are
saved in database servers by means of OLTP systems. Now, pinnacle executives need access to
statistics primarily based on statistics to base their decisions on. This is where on line analytical
processing or OLAP systems enter the picture. Data warehouses and different OLAP systems are
built an increasing number of due to this very need of or top executives. We don’t most effective
want data however additionally the analytics associated with it to make better and more
profitable selections. OLTP and OLAP structures work in tandem.
Data has end up part of every side of business and lifestyles (Usman and et.al., 2021).
Companies today can harness facts mining programs and system getting to know for everything
from enhancing their income procedures to decoding financials for funding purposes. As a result,
information scientists have end up essential to corporations all around the global as corporations
are searching for to reap bigger dreams than ever earlier than. Data mining can solution business
questions that had been traditionally not possible to reply due to the fact they have been too time-
eating to remedy manually. Using effective computers and algorithms to execute various
statistical techniques that analyze information in distinctive methods, customers can identify
patterns, developments, and relationships they could in any other case omit. They can then
practice these findings to expect what is possibly to take place inside the destiny and take
movement to influence commercial enterprise effects. Data mining is used in lots of areas of
commercial enterprise and studies, together with sales and advertising and marketing, product
improvement, healthcare, and education (Virtanen, 2021). When used efficiently, statistics
mining can come up with an advantage over competitors through making it feasible to examine
extra about clients, develop effective advertising strategies, increase sales, and decrease prices.
What are the advantages and disadvantages of data mining in crime rate prediction?
As per the viewpoint of Chauhan and Sehgal (2017), there are various advantages and
disadvantages of data mining in crime rate prediction. The advantages which can be gained
through data mining in crime rate prediction is that it helps in gathering reliable information, cost
effective solution as compared to other data prediction, detect crime, analyse enormous amount
of data quickly, predict the future crime and discover hidden patterns. These advantages help to

make informed decisions and also detect risks and fraud. Data mining helps in uncovering the
hidden facts and information from large database of criminal records for controlling, preventing
and investigating the crime for particular case. Crime data is useful for police department as it
can reduce their workload. The information gathered through data mining can also be used for
local crime pattern analysis and also provide insight view of well reported and well recorded
crime. There are several disadvantages of data mining in crime rate prediction which can
influence the performance of police department. It is important for police force to identify the
challenges faced by using data mining techniques in crime rate prediction. The disadvantage of
data mining tools are its costly process, need to conduct accurately, data can be misused,
complex and challenging to use and also it requires large database.
Predictive policing refers to the use of predictive analytics primarily based on mathematical
fashions, and different analytical strategies in law enforcement to become aware of potential
crook hobby. Predictive policing consequently makes use of pc structures to research massive
sets of records to assist decide where to install police or to pick out folks that are purportedly
more likely to commit or be a victim of against the law. Predictive policing has the potential to
make policing fairer (Xu and et.al., 2021). By promoting selection-making based on objective
proof, predictive policing should probably alleviate positive discrepancies within the
enforcement of the regulation. When the set of rules compiled the crime hot spot map within the
above-referred to LA have a look at, it did now not directly rely on prejudice. By contrast, the
traditional LAPD hot spot maps were produced via (inevitably prejudiced) people. As a end
result, algorithms is probably capable of help law enforcement officials to higher predict
dangers, determine the identification of offenders, and identify the vulnerabilities of a
community and its contributors. However, this capability can simplest be realised if the set of
rules is indeed freed from bias, which isn't always the case. Crime facts is useful for police
branch as it is able to lessen their workload. The statistics amassed through information mining
also can be used for local crime pattern analysis and additionally provide perception view of
properly reported and nicely recorded crime. There are several hazards of information mining in
crime fee prediction which can affect the performance of police branch. It is crucial for police
pressure to identify the challenges confronted through using information mining strategies in
crime price prediction. The disadvantage of records mining gear are its high priced manner, want
to behavior as it should be, information can be misused, complicated and hard to apply and also

it calls for large database. Predictive policing refers to using predictive analytics primarily based
on mathematical models, and distinct analytical strategies in regulation enforcement to emerge as
privy to capacity crook interest. Data mining facilitates in solving the crimes quicker and this
approach offers good results whilst implemented on crime dataset, the records acquired from the
data mining techniques can assist the police department. Produce crucial outcomes from crime
document datasets. The very step in study of crime is crime evaluation.
When it involves the use of records to predict crime warm spots, many years of
criminology studies show that crime reviews and different data amassed by using the police
frequently report law enforcement’s response to the reviews they receive and situations they
come upon, in preference to imparting an objective or complete record of all of the crimes that
occur. Put in any other case, the databases inform us where crime has been spotted by way of the
police in the past. But we recognise that in many nations the police devote disproportionate
assets concentrated on sure minority organizations. The end result is that racist practices get
baked into the statistics. Predictive policing then becomes a vicious circle. Crime records
gathered on the idea of a racist coverage will create racist predictions, leading to over-policing so
that the can hold to generate misleading records and racist predictions. Predictive policing
structures exacerbate this trend by making policing merely approximately the numeric discount
of the fees of detected, rather than actual, crime.
How data mining helps in prediction of crime rate in UK?
As per the viewpoint of Yerpude (2020), data mining helps in predicting the crime rate in
UK by increasing advent of computerized system. Through using data mining, crime data analyst
can also help law enforcement officers to solve the crimes in effective and efficient manner. By
implementing the concept of data mining, the police department can gain useful and unknown
information from unstructured data. Data mining is used for predicting the crime rate in UK by
using artificial intelligence methods due to which the crime data can be automatically entered
into database. Classification is one of the techniques of data mining that helps in predicting about
the values of data (Farsi and et. al., 2018). The crime is predicted through field research and
content analysis. In field research, police forces focus on observing characteristics of locations
whereas content analysis is used for examining the police report narratives. Link analysis is
known as data mining technique which is useful in the detection of valid and useful patterns. The
main framework of this analysis totally depends upon the fact of linking one activity with

another. Data mining is a manner to extract information out of normally big information units; in
other phrases it's far an technique to find out hidden relationships amongst statistics via using
artificial intelligence methods. By the usage of this technique, crime facts can be mechanically
entered into a database, in law enforcement companies.
Data mining techniques are used to operate on large amount of data to discover hidden
patterns and relationships helpful in decision making. Classification is a predictive data mining
technique, makes prediction about values of data using known results found from different data.
The five basic steps in the crime analysis process include the collection, categorization, analysis,
dissemination, and evaluation of information. The reports and forms for use in each step of
information-gathering and analysis are explained and illustrated. Crime forecasting refers to the
basic process of predicting crimes before they occur. Tools are needed to predict a crime before
it occurs. Currently, there are tools used by police to assist in specific tasks such as listening in
on a suspect's phone call or using a body cam to record some unusual illegal activity. Predicting
Crime and Other Uses of Neural Networks in Police Decision Making. Neural networks are a
machine learning method that excel in solving classification and forecasting problems. They
have also been shown to be a useful tool for working with big data oriented environments such
as law enforcement. Criminology is one of the maximum crucial fields for applying statistics
mining. Criminology is a process that aims to discover crime characteristics. Actually crime
analysis includes exploring and detecting crimes and their relationships with criminals. Data
mining allows to expand clever market selection, run accurate campaigns, make predictions, and
greater; With the assist of Data mining, we're in a position to research consumer behaviors and
their insights. This effects in exceptional achievement and statistics-driven commercial
corporation. Use of records mining techniques can produce vital consequences from crime report
datasets. The very step in have a take a look at of crime is crime analysis. Crime assessment is
exploring, inter pertaining to and detecting courting the various numerous crimes and traits of the
crime. This analysis permits in making equipped statistics, queries and maps on demand. It
additionally permits to peer if a criminal offense in a certain recognized sample or a brand new
pattern essential.
Data mining is a way to extract expertise out of usually massive facts sets; in different
words it's miles an technique to find out hidden relationships among facts with the aid of the use
of synthetic intelligence methods. By the use of this method, crime facts may be automatically

entered right into a database, in regulation enforcement organizations. Crime analysts use
computer non-public computers to finish most in their work and use laptop computer systems for
fieldwork and shows. In many cases, police departments' crime analysis gadgets have their very
own servers to house tabular data, geographic facts, and software program packages which are
shared by means of customers. Broadly speaking, there are packages of predictive policing.
First, the use of arrest facts to predict geographical crime hotspots. Second, mined social media
facts that can be used to determine how probable it's far that a person would possibly dedicate
against the law. The prediction problem is based totally on an emphasis in discretionary choice
making by judges relating sentencing (Yen and Hung, 2021). The criminal justice device is based
off of discretionary regular choices that involve predictions about destiny conduct. Predicting
Crime and Other Uses of Neural Networks in Police Decision Making. Neural networks are a
device mastering approach that excel in solving category and forecasting problems. They have
additionally been proven to be a useful tool for working with huge information oriented
environments which include regulation enforcement.
Crime is one among transcendent and demanding a part of our general public. Ordinary top notch
number of violations are finished, these successive wrongdoings have made the existences of
ordinary citizens anxious. Thus, preserving the crime from occurring is a essential project. In the
new time, it's miles seen that automated reasoning has proven its significance in almost all the
field and wrongdoing expectation is one in every of them. However, it's miles expected to
preserve the best data set of the wrongdoing that has befell as this facts may be utilized for
destiny reference (Yen and Hung, 2021). The potential to foresee the wrongdoing which could
take place in destiny can assist the regulation authorization workplaces in forestalling the
wrongdoing earlier than it happens. The potential to count on any wrongdoing based totally on
time table, region, and so forth can help in giving valuable records to law authorization from
strategical standpoint. In any case, looking forward to the wrongdoing precisely is a tough errand
due to the fact violations are increasing at a demanding charge. Hence, the wrongdoing
expectation and research strategies are essential to understand the future violations and lessen
them. In Late time, severa experts have led analyses to expect the violations making use of
distinctive AI strategies and specific facts assets. For crime expectation, KNN, help vector
device and linear regression and a few extraordinary calculations are The website online is
slithered utilizing a creeping software written in python language, and the records is placed away

in an impermanent information set. Utilizing 3 particular classifiers, the statistics is grouped into
wrongdoing related statistics and non-wrongdoing related facts. The eventual outcome suggests
that fundamental calculations can be powerful inside the undertaking of wrongdoing forecast.
Crime is certainly one of transcendent and disturbing part of our preferred public.
Ordinary first-rate variety of violations are completed, those successive wrongdoings have made
the existences of ordinary citizens worrying. Thus, keeping the crime from taking place is a
essential challenge. In the new time, it is visible that automated reasoning has tested its
significance in nearly all of the subject and wrongdoing expectation is one in every of them.
However, it's far expected to keep the exceptional information set of the wrongdoing that has
happened as this information may be utilized for destiny reference. The potential to foresee the
wrongdoing that may take region in future can assist the regulation authorization offices in
forestalling the wrongdoing earlier than it takes place. The capacity to assume any wrongdoing
primarily based totally on time desk, location, and so on can help in giving treasured information
to law authorization from strategical point of view. In any case, looking ahead to the wrongdoing
exactly is a hard errand due to the reality violations are growing at a disturbing fee.
Crimes are normal social issues that impact the non-public delight, economic
development and notoriety of a state. Information mining offers fantastic techniques and
calculations to break down records and concentrate large information from it. Analysing this
statistics no longer just aides in perceiving an detail in rate of horror price yet similarly enables
in taking critical activities for aversion of violations. Wrongdoing is an indication that is culpable
and which makes harm other guiltless people and concrete regions. Violations are of numerous
kinds like robbery, murder, attack, assault, rape sexual, mind torture, bogus detainment,
hijacking, crime and a few extra. An investigation is the method for profoundly assessing the
constituent components or constructions of the item or subjects viable. The exam is done with
the purpose of obtaining an exhaustive comprehension of a field.
Methodology
Research methodology is defined as procedure or tool that is used by investigator and researcher
for determining, selecting and analyzing data or information associated with the research topic
(Yen and Hung, 2021). Research methodology can be depict as the act of researcher to track
down right and precise information regarding the research subject. This is the interaction that
should be consider by researcher for easily and successfully performing research rehearses.

There are various angles that are connected with research methodology through which required
set of information could be gathered by researcher. In context of present research, this is
conducted using valid and reliable sources. Various type of research methods used in conducting
this study are mentioned below –
 Qualitative data analysis: This is defined as research in which information and data is
in the form of interpretations. The different types of qualitative data include ethnography,
narrative, phenomenological and many more. Different ways by which qualitative data is
collected includes one to one interview, case study analysis, record keeping, focus group,
and many more.
 Quantitative data analysis: This is defined as data in which information and data is
denoted or expressed in context of numbers. Data collected through numeric and statistics
is used in quantitative analysis.
In respect of respective research, investigator is adopting qualitative data analysis method
as through that they able to gather reliable and authenticate information.
 Research philosophy: The research philosophy is associated with nature, sources and
knowledge associated with research topic. An investigator can adopt research philosophy
for collecting authentic and reliable data for the research. In respect of this research,
researcher is adopting positivism philosophy as that depend on the quantitative

information which are collected by researcher. This is refer as the conviction as indicated
by which different research rehearses should be performed. There are various sorts of
research philosophy, for example, of the positivism, interpretivisim, authenticity as well
as the rationale type. In this research, investigator is thinking about positivism research
philosophy. This will be best and precise research philosophy that will help in tracking
down significant information regarding the research subject.
 Research approaches: This is known as process or plan that includes different steps for
broadening the scope of research. as plan as well as process which consist different steps
related to broad assumptions of research. The nature of research project is divided into
two parts - inductive and deductive. The present investigator has adopted deductive
research approach. This provides help in easily understanding concept of research topic.
 Research design: It is considered as frameworks of research method or technique that
investigator uses for completing the research work. It is essential that research design is
properly understood. It is significant for researcher to think about right and precise
research plan to track down suitable information regarding the research. There are
various kinds of research plan, for example, deductive and inductive. In this research
practice, investigator is thinking about deductive research plan. Through this research
plan, investigator will actually want to zero in on recognizing inside and out and suitable
information about the research subject This will help with tracking down required set of
information in legitimate and orderly way.
 Data collection methods: This is considered as the process which is used by project
manager for collecting information over various sources that has been helping in
developing picture over research interest. There are two kinds of data collection method
which are primary and secondary. Both of these are urgent and fundamental for
successfully directing research practices and assembling required set of information.
Primary strategy for data assortment is allude as the most common way of observing
direct information that is never utilized. This will be huge for researcher to track down
precise and important information regarding the research theme. Where as secondary data
assortment can be depict as the approach to tracking down information that has been
gathered before in the primary strategy. This will be cost and efficient approach for
researcher. It is used over describing techniques over information gathered by research

from first hand sources as well as it is time taking process. On the other hand, information
gathered through secondary method are already present so it is less time taking.
Discussion and Findings
Discussion
Theme 1 Concept of data mining and it’s usage in crime
Data mining is the process used for extraction of information from a given data set in
order to identify patterns and trends of data. This helps to make data supported decisions from
different data sets. This concept works in conjunction with predictive analysis. This comes under
statistical science branch which uses complex algorithms designed for working with group of
problems. Predictive analysis is used in identifying patterns for huge amount of data. This is used
for generalizing forecasts and predictions. There are various efforts used for automated
techniques in order to analyze different crime types. The relationship between crime type and
capability help investigators in identifying the techniques efficiently. The framework provides
set of techniques where neural network is used for crime analysis. businesses can profit
information for reducing crime, increasing revenue, reducing risk and further developing client
relationship. Police department fundamentally use data mining instruments in crime rate
prediction and get data through cleaning crude data, making models and tracking down designs.
Crime is unquestionably one of otherworldly and upsetting aspect of our favored public. propels
in handling power and speed have empowered us to move beyond manual, tedious and tedious
practices to fast, simple and robotized data investigation. The more complicated the data sets
gathered, the more potential there is to reveal important bits of knowledge. Retailers, banks,
manufacturers, telecommunications suppliers and safety net providers, among others, are
utilizing data mining to find connections among everything from value advancement,
advancements and socioeconomics to how the economy, risk, contest and web-based media are
influencing their plans of action, revenues, activities and client connections. The social network
analysis provides facilitation for crime association and pattern visualization. Crime rate is
calculated by dividing total crime density of state with total population of that state since police
of state are known as efficient when crime rate in low. This means that output function of crime
is high. There are clustering techniques that were evaluated in efficiency for formulating accurate
clusters, efficiency of identifying crime trends, speed of creating clusters and identifying crime
zones. By using data mining, different algorithms and techniques are available for analyzing and

scrutinizing data. On the basis of situation, techniques are used solely and it depends upon
circumstances.
The applications of data mining use different parameters for examining data start
investigation. It is likely caused by attack and individuals may be responsible for attack. Crime
investigations have remained prerogative for law enforcement concern. Entity extraction is used
automatically for identifying address, person and vehicle. Deviation detection is applied in
context of fraud detection and network intrusion detection. Classification is used for detecting
email spamming and finding authors who are involved in unsolicited emails. When classification
is used, the existing dataset can be used easily. There is no help involved in predicting how new
individuals have behaved on the basis of classification criteria. Data mining creates classification
models by examination of already existing data. The cases can be of historical database which
includes individuals who have already undergone specific medical treatment. Data mining is a
way to extract information out of typically massive statistics units; in different words it's miles
an technique to discover hidden relationships amongst facts by using the use of synthetic
intelligence methods. By the use of this method, crime records can be robotically entered right
into a database, in regulation enforcement corporations. Predictive policing reduces the duty of
law enforcement. As most strategies in records analytics are computerized, it might undermine
the ability of officers and departments to explain and justify their selections in a meaningful way.
Data mining allows in fixing the crimes faster and this approach offers suitable results when
carried out on crime dataset, the information obtained from the information mining strategies can
assist the police branch. Produce crucial results from crime report datasets. The very step in take
a look at of crime is crime analysis.
Data mining is a way to extract understanding out of typically large records units; in
different words it is an technique to discover hidden relationships among records by using using
artificial intelligence techniques. By using this method, crime statistics may be automatically
entered into a database, in regulation enforcement groups. Criminology is one of the maximum
crucial fields for applying data mining. Criminology is a manner that pursuits to discover crime
traits. Actually crime analysis consists of exploring and detecting crimes and their relationships
with criminals. The high quantity of crime datasets and additionally the complexity of
relationships among these sorts of data have made criminology the proper field for making use of
data mining strategies. Identifying crime characteristics is the first step for developing similarly

analysis. The expertise this is gained from records mining strategies is a totally useful device that
can help and help police forces. An method based on facts mining techniques is mentioned on
this paper to extract critical entities from police narrative reports which are written in plain
textual content.
Crimes are a worldwide trouble which could damage the kingdom underneath both social
and economic situations. Crime control is an inescapable step. It is obligatory for the welfare and
sustainable improvement of a kingdom. We recognize very well that within the digital
international, it isn't always an clean challenge to expose the criminals and the inclined areas
which can be continuously getting tormented by their wrongdoings. The police departments of
every state also are constantly running in a paced way to conquer the crimes, criminals and their
techniques. The trouble in investigating a massive quantity of statistics regarding crimes and
criminals has come to be a prime assignment for police department officers. An technique is
needed that can classify, systematically look at and forecast the crimes that help to lessen the
crime fee. There are diverse methodologies and paradigms so one can help police officials to
discover and do away with crimes from society. Data mining empowers us with several realistic
and handy approaches to evaluate massive and distinct units of information. It helps to discover
hidden information from the huge database of criminal information for investigating, controlling
and stopping crime for organizations and customers. Various researchers and statistics analysts
gave their treasured time and information to the sphere of information mining. The paper focuses
primarily on presenting a short and snappy evaluate of various research papers targeted at the
techniques of statistics mining that have been applied in crime analysis.
Theme 2: Importance to use data mining technique in crime rate prediction
Data mining facilitates in solving the crimes quicker and this approach offers good results
whilst implemented on crime dataset, the records acquired from the data mining techniques can
assist the police department. Produce crucial outcomes from crime document datasets. The very
step in study of crime is crime evaluation. Data mining is a manner to extract information out of
typically large statistics sets; in different words it's far an technique to find out hidden
relationships amongst statistics by way of using artificial intelligence techniques. The wide range
of information mining programs has made it an critical discipline of studies. Criminology is one
of the maximum vital fields for applying statistics mining. Criminology is a system that
ambitions to discover crime characteristics. Actually crime evaluation includes exploring and

detecting crimes and their relationships with criminals. The prediction of future crime patterns
includes following crime rate changes over time and utilized data mining to extend those changes
into what's to come. The fundamental technique includes bunch the states having a similar crime
pattern and afterward utilizing next year group information to characterize records. This is joined
with the state destitution data to make a classifier that will anticipate future crime patterns. To
the grouped outcomes, an arrangement algorithm was applied to anticipate the future crime
design. The characterization was performed to find in which class a group would be in the
following year. This permits us to assemble a prescient model on foreseeing next year‟s records
utilizing this year‟s data. The choice tree algorithm was utilized for this reason. The high volume
of crime datasets and also the complexity of relationships among those kinds of facts have made
criminology the right subject for making use of facts mining strategies.
Data mining enables to broaden smart marketplace selection, run accurate campaigns,
make predictions, and greater; With the assist of Data mining, we are able to analyze consumer
behaviors and their insights. This results in incredible achievement and statistics-driven
commercial enterprise. Use of information mining strategies can produce vital results from crime
file datasets. The very step in have a look at of crime is crime analysis. Crime evaluation is
exploring, inter relating and detecting relationship among the numerous crimes and traits of the
crime. This analysis enables in making ready information, queries and maps on demand. It
additionally allows to see if a crime in a certain known pattern or a new pattern vital. Crime is
one among transcendent and traumatic a part of our widespread public. Ordinary high-quality
range of violations are executed, those successive wrongdoings have made the existences of
ordinary residents irritating. Thus, preserving the crime from taking place is a fundamental
assignment. In the brand new time, it's far seen that automated reasoning has proven its
significance in almost all of the area and wrongdoing expectation is one in every of them.
However, it's miles expected to keep the perfect statistics set of the wrongdoing that has took
place as this information can be applied for destiny reference. The potential to foresee the
wrongdoing which could take place in future can assist the law authorization places of work in
forestalling the wrongdoing earlier than it takes place. The potential to anticipate any
wrongdoing based on time table, region, and so on can assist in giving treasured statistics to law
authorization from strategical viewpoint. In any case, anticipating the wrongdoing exactly is a
difficult errand given that violations are expanding at a traumatic price.

The equal techniques used in analysing excessive volume crime can be used to link low
volume most important crimes along with severe sexual attacks. The recognised advantages
include an development in the accuracy of effects over cutting-edge semi-guide methods and a
discount within the time taken to reap those consequences. Police information is inconsistent
and, often, incomplete making the project of formal analysis some distance greater tough and
present day analytical practices are semi-manual and time consuming generating results of
restricted accuracy. Entity extraction has been utilized to consequently recognize person,
address, vehicle, narcotic drug, and personal properties from police story reports. Clustering
procedures have been utilized to consequently relate various articles, for example, persons,
associations, vehicles in crime records. Deviation detection has been applied in extortion
detection, network interruption detection, and other crime investigations that include following
strange exercises. Order has been utilized to recognize email spamming and observe creators
who convey spontaneous messages. These analytical techniques would gain from the use of facts
mining techniques inside a dependent technique as mentioned inside this thesis. The utilization
of supervised and unsupervised mastering strategies within a based methodology to mining
Police records is evaluated. Criminology is one of the maximum crucial fields for applying
statistics mining. Criminology is a process that aims to discover crime characteristics. Actually
crime analysis includes exploring and detecting crimes and their relationships with criminals.
Data mining allows to expand clever market selection, run accurate campaigns, make
predictions, and greater; With the assist of Data mining, we're in a position to research consumer
behaviors and their insights. This effects in exceptional achievement and statistics-driven
commercial corporation. Use of records mining techniques can produce vital consequences from
crime report datasets. The very step in have a take a look at of crime is crime analysis. Crime
assessment is exploring, inter pertaining to and detecting courting the various numerous crimes
and traits of the crime. This analysis permits in making equipped statistics, queries and maps on
demand. It additionally permits to peer if a criminal offense in a certain recognized sample or a
brand new pattern essential.
Accurate actual time crime predictions assist as much as a more extent to reduce crime
price however it's far a hard hassle as occurrences of crime relies upon on many factors. Various
visualizing techniques and machine studying algorithms are followed for predicting the crime
distribution in an area. The uncooked records sets have been processed and visualized primarily

based mostly on desires In the first step and later gadget learning algorithms were used to extract
expertise out of the large datasets and locate the hidden relationships amongst statistics that's in
addition used to for reporting and discovering the crime styles which can be very essential
deliver of statistics for crime analyst to research those crime networks via numerous interactive
visualizations for crime prediction and as a end result is very supportive in preventing crime.
Theme 3: How data mining helps in prediction of crime rate in UK
Data mining is a manner to extract knowledge out of typically massive information sets;
in other words it's far an technique to find out hidden relationships amongst statistics by using
artificial intelligence techniques. By the usage of this technique, crime data may be automatically
entered right into a database, in regulation enforcement corporations. Data mining strategies are
used to function on big quantity of facts to find out hidden styles and relationships helpful in
selection making. Classification is a predictive information mining technique, makes prediction
about values of information the usage of recognized results located from exceptional statistics.
The increase in crime records recording coupled with information analytics resulted inside the
growth of studies techniques aimed at extracting understanding from crime facts to better
recognize crook conduct and in the long run prevent future crimes. While a lot of those strategies
make use of clustering and affiliation rule mining strategies, there are fewer strategies
specializing in predictive models of crime. The most commonplace social trouble faced
nowadays everywhere in the international are crimes. Quality of lifestyles, monetary boom and
reputation of nation are substantially suffering from crimes.
There has been increase in the crime charge because last few years and so that the can
lessen this crime charge government wishes to take the preventive measures. In order to secure
the society from such crimes there may be a want to adopt new advanced device and techniques
to improve crime analytics for protecting the community. Accurate real time crime predictions
help up to a greater extent to reduce crime price however it's far a challenging trouble as
occurrences of crime relies upon on many factors. Various visualizing strategies and gadget
gaining knowledge of algorithms are followed for predicting the crime distribution in an area.
The uncooked records sets have been processed and visualized based totally on wishes In step
one and later machine gaining knowledge of algorithms were used to extract know-how out of
the massive datasets and locate the hidden relationships amongst records which is further used to
for reporting and coming across the crime patterns which may be very important supply of

statistics for crime analyst to research those crime networks through various interactive
visualizations for crime prediction and as a result is very supportive in stopping crime.
Crime prediction is becoming a quick-developing area of studies within the field of
records technology because of an growth inside the availability of crime information and the
adoption of facts-driven tactics via police departments all over the international. The exploration
of crime evaluation has been done from quite some viewpoints the usage of a few extraordinary
styles of datasets. Some researchers used a London-based crime dataset in conjunction with a
dataset of geo-localized records together with transportation, families and London borough
profiles as well as a behavioural dataset computed from cell community hobby within the town.
A few papers used the Denver crime dataset to identify forms of crime going on in exclusive
regions and additionally to identify patterns and correlations among various factors. These are
then used to both are expecting destiny crime or to discover hotspots in which specific varieties
of crime are taking place.
With the increasing introduction of automated structures, crime information analysts can
assist the Law enforcement officials to speed up the technique of solving crimes. Using the idea
of facts mining we are able to extract previously unknown, useful records from an unstructured
statistics. Crime analysis and prevention is a scientific technique for figuring out and reading
patterns and tendencies in crime. Our machine can are expecting regions that have excessive
possibility for crime incidence and might visualize crime prone regions. With the increasing
creation of computerized structures, crime facts analysts can assist the Law enforcement officers
to hurry up the method of solving crimes. Using the idea of records mining we are able to extract
formerly unknown, beneficial facts from an unstructured facts. Here we have an approach
between computer technological know-how and justice to increase a data mining process that can
help solve crimes faster. Instead of specializing in causes of crime prevalence like criminal
heritage of offender, political enmity and so forth we are focusing specifically on crime elements
of every day.
Different varieties of system gaining knowledge of and records mining algorithms had
been used in crook evaluation (Yen and Hung, 2021). Both supervised and unsupervised
mastering techniques have been used but there may be really greater research in supervised
algorithms. This is plain as most crime statistics available is ancient facts which offers us clear
outputs and consequently supervised algorithms have been effective in crime prediction. The

fashions trying to determine crime styles require unsupervised gaining knowledge of fashions to
be applied. There is tremendous research in different components of the sector however there's
nonetheless room for development. One paper used cellular network interest, demographic
records and crook data to perceive crime hotspots within the town of London. They divided the
metropolis of London into grids and then classified every mobile in the grid as both a criminal
offense hotspot or no longer.
The increase in crime data recording coupled with information analytics resulted within
the growth of studies procedures geared toward extracting understanding from crime statistics to
higher understand crook conduct and ultimately prevent destiny crimes. While many of these
techniques employ clustering and association rule mining techniques, there are fewer approaches
that specialize in predictive fashions of crime. Data mining is a manner to extract knowledge out
of usually big information units; in different words it's far an approach to find out hidden
relationships among statistics by using artificial intelligence techniques. By the usage of this
technique, crime information can be mechanically entered right into a database, in law
enforcement agencies. Data mining is a manner to extract expertise out of normally big records
units; in different phrases it is an method to find out hidden relationships amongst information
through using synthetic intelligence techniques. The wide variety of information mining
packages has made it an critical subject of studies. Criminology is one of the maximum critical
fields for making use of facts mining. Criminology is a technique that goals to become aware of
crime characteristics. Actually crime evaluation consists of exploring and detecting crimes and
their relationships with criminals. The excessive volume of crime datasets and also the
complexity of relationships between these sorts of records have made criminology the perfect
subject for applying facts mining techniques. Identifying crime characteristics is the first step for
growing similarly analysis. The information that is gained from information mining processes is
a very useful device which could help and assist police forces.
Data mining is used to offer two number one benefits: to offer businesses the predictive
electricity to estimate the unknown or destiny values and to offer corporations the descriptive
energy by using locating thrilling styles in the information. Predictive analytics are used to
acquire and are expecting future results and trends. Data mining is the method of coming across
beneficial styles and trends in massive records units. Predictive analytics is the procedure of
extracting information from huge datasets with a purpose to make predictions and estimates

approximately destiny results. Crime analysts use laptop personal computer systems to complete
most of their paintings and use laptop computers for fieldwork and shows. In many instances,
police departments' crime analysis devices have their personal servers to residence tabular
information, geographic statistics, and software programs which might be shared through
customers.
Conclusion and Recommendations
Conclusion
From the above report, it can be concluded that data mining is an activity of identifying
patterns, correlation and anomalies within wide range of data sets in order to predict results.
Through using data mining technique, businesses can avail information for reducing crime,
increasing revenue, reducing risk and improving customer relationship. It has been identified that
Data mining mainly includes machine learning, statistics and database system. There are various
types of data mining such as clustering, association, data cleaning, data visualization,
classification, machine learning, prediction and neural networks. Police department mainly use
data mining tools in crime rate prediction and understand data through cleaning raw data,
creating models and finding patterns. It is important to use data mining techniques in crime rate
prediction as it helps in solving the crime in effective and efficient manner. Through data mining
technique, the crime department can also avail productive results when applied on crime
datasets. The information which is availed by data mining technique helps in producing
significant outcomes from crime report datasets. It is used in crime rate prediction for exploring
and detecting crime and their relationship with criminals.
The crime is predicted through field research and content analysis. In field research, police forces
focus on observing characteristics of locations whereas content analysis is used for examining
the police report narratives. Link analysis is known as data mining technique which is useful in
the detection of valid and useful patterns (Yen and Hung, 2021). The main framework of this
analysis totally depends upon the fact of linking one activity with another. Data mining is a
manner to extract information out of normally big information units; in other phrases it's far an
technique to find out hidden relationships amongst statistics via using artificial intelligence
methods. By the usage of this technique, crime facts can be mechanically entered into a database,
in law enforcement companies. It has been determined that Data mining applied with regards to
regulation implementation and insight investigation holds the guarantee of reducing crime

related issue. Utilizing a wide scope of procedures it is feasible to find helpful information to aid
crime matching, not just of single crimes, yet in addition of series of crimes. In this paper we
utilize a clustering/arrange based model to expect crime patterns. The data mining methods are
utilized to investigate the crime data from database. The aftereffects of this data mining might
actually be utilized to decrease and even forestall crime for the approaching years. we accept that
crime data mining has a promising future for increasing the adequacy and proficiency of criminal
and insight examination. The crime is predicted through field research and content analysis. In
field research, police forces focus on observing characteristics of locations whereas content
analysis is used for examining the police report narratives. Link analysis is known as data mining
technique which is useful in the detection of valid and useful patterns. The main framework of
this analysis totally depends upon the fact of linking one activity with another.
Recommendations
There are certain recommendations that can be made to ensure that data mining is used in
an effective manner in criminology. Data mining is a manner to extract knowledge out of
commonly huge facts sets; in different phrases it is an approach to discover hidden relationships
among information by way of the usage of artificial intelligence techniques. The huge range of
statistics mining programs has made it an important discipline of studies. Neural networks are a
machine mastering method that excel in solving type and forecasting problems. They have also
been shown to be a beneficial device for operating with huge statistics oriented environments
including law enforcement. This article reviews and examines current studies at the utilization of
neural networks for forecasting crime and other police decision making hassle solving. Neural
community fashions to expect unique forms of crime the usage of place and time information
and to predict a crime’s area when given the crime and time of day are advanced to demonstrate
the application of neural networks to police choice making.
Data mining is used to offer two number one benefits: to offer businesses the predictive
electricity to estimate the unknown or destiny values and to offer corporations the descriptive
energy by using locating thrilling styles in the information. Predictive analytics are used to
acquire and are expecting future results and trends. Data mining is the method of coming across
beneficial styles and trends in massive records units. Predictive analytics is the procedure of
extracting information from huge datasets with a purpose to make predictions and estimates
approximately destiny results. Crime analysts use laptop personal computer systems to complete

most of their paintings and use laptop computers for fieldwork and shows. In many instances,
police departments' crime analysis devices have their personal servers to residence tabular
information, geographic statistics, and software programs which might be shared through
customers.
Limitations of the research
When carrying out a have a look at, it's miles vital to have a enough sample size a good way
to draw legitimate conclusions. The larger the sample, the more specific the consequences will
be. If the pattern length is just too small, it is going to be difficult to perceive giant relationships
in the data. Citing and referencing previous studies research constitutes the idea of the literature
assessment in the thesis or have a look at, and those prior research offer the theoretical
foundations for the research query the are investigating. However, relying on the scope of the
studies subject matter, previous studies studies which can be relevant to thesis is probably
constrained. After entire analysis of the studies findings , one may recognize that the manner in
which the have accumulated the records or the methods in that the have measured variables has
restrained the capability to behavior a thorough analysis of the effects.
Researchers may maintain biased perspectives because of their cultural backgrounds or
views of sure phenomena, and this can have an effect on a take a look at’s legitimacy. Also, it is
possible that researchers will have biases closer to information and effects that simplest assist
their hypotheses or arguments. In order to keep away from those problems, the writer(s) of a
study ought to take a look at whether or not the manner the trouble changed into stated and the
information-accumulating process had been accomplished correctly. After acknowledging ther
boundaries, the want to speak about some viable methods to conquer those limitations in future
research. One manner to do this is to present opportunity methodologies and ways to keep away
from issues with, or “fill in the gaps of” the restrictions of this study the've got presented.
Discuss each the professionals and cons of these options and in reality provide an explanation for
why researchers need to select those processes. Make positive the're present day on procedures
used by earlier studies and the impacts they've had on their findings. Cite evaluate articles or
clinical our bodies which have endorsed those processes and why. This is probably proof in aid
of the approach the chose, or it might be the reason the recollect the selections to be obstacles.
This process can act as a justification in ther approach and a defense of ther choice to take it
whilst acknowledging the feasibility of different strategies.

The boundaries of the study are the ones traits of design or technique that impacted or
inspired the interpretation of the findings out of the research. The boundaries of a take a look at
are its flaws or shortcomings which could be the result of unavailability of resources, small
sample length, flawed technique, and many others. No examine is absolutely faultless or together
with all feasible components. Therefore, list the restrictions of ther have a look at displays
honesty and transparency and also suggests that the have a entire information of the subject.
Generally speaking, the restrictions are introduced in the Discussion segment, simply before the
concluding paragraph. While the should in reality factor out the constraints, do now not get into
an intricate discussion about them. Keep this segment short and crisp. The barriers of a look at
basically discusses any unanswered questions that ther have a look at did now not cope with.

REFERENCES
Books & Journals
Agarwal, A. and et.al., 2020. Fake news detection using a blend of neural networks: An
application of deep learning. SN Computer Science, 1(3), pp.1-9.
Alam, T.M. and et.al., 2021. Corporate bankruptcy prediction: An approach towards better
corporate world. The Computer Journal, 64(11), pp.1731-1746.
Borowik, G., Wawrzyniak, Z.M. and Cichosz, P., 2018, December. Time series analysis for
crime forecasting. In 2018 26th International Conference on Systems Engineering
(ICSEng) (pp. 1-10). IEEE.
Brayne, S., 2020. Predict and surveil: Data, discretion, and the future of policing. Oxford
University Press, USA.
Chamikara, M.A.P. and et.al., 2020. Efficient privacy preservation of big data for accurate data
mining. Information Sciences, 527, pp.420-443.
Das, P. and Das, A.K., 2019. Application of classification techniques for prediction and analysis
of crime in India. In Computational Intelligence in Data Mining (pp. 191-201).
Springer, Singapore.
Dutta, S., Roy, S., and Kole, D.K., 2022. Application of Social Networks and Data Mining on
Crime Victims. In Proceedings of International Conference on Advanced Computing
Applications (pp. 693-703). Springer, Singapore.
Fouad, H. and et.al., 2020. Analyzing patient health information based on IoT sensor with AI for
improving patient assistance in the future direction. Measurement, 159, p.107757.
Islam, M.R. and et.al., 2021. Discovering dynamic adverse behavior of policyholders in the life
insurance industry. Technological Forecasting and Social Change, 163, p.120486.
Kaur, B., Ahuja, L. and Kumar, V., 2019, February. Crime against women: Analysis and
prediction using data mining techniques. In 2019 International Conference on Machine
Learning, Big Data, Cloud and Parallel Computing (COMITCon) (pp. 194-196). IEEE.
Marmo, R., 2021. Data Mining for Fraud Detection. In Encyclopedia of Organizational
Knowledge, Administration, and Technology (pp. 1150-1162). IGI Global.
Mason, S.J., 2019. Analysis of Virginia Crime Data of the Year 2016 Using Data Mining
Techniques (Doctoral dissertation, North Carolina Agricultural and Technical State
University).
Raza, D.M. and Victor, D.B., 2021, March. Data mining and Region Prediction Based on Crime
Using Random Forest. In 2021 International Conference on Artificial Intelligence and
Smart Systems (ICAIS) (pp. 980-987). IEEE.
Singh, N., Bellathanda Kaverappa, C. and Joshi, J.D., 2018, July. Data mining for prevention of
crimes. In International Conference on Human Interface and the Management of
Information (pp. 705-717). Springer, Cham.
Sivanagaleela, B. and Rajesh, S., 2019, April. Crime analysis and prediction using fuzzy c-means
algorithm. In 2019 3rd International Conference on Trends in Electronics and
Informatics (ICOEI) (pp. 595-599). IEEE.
Usman, N. and et.al., 2021. Intelligent dynamic malware detection using machine learning in IP
reputation for forensics data analytics. Future Generation Computer Systems, 118,
pp.124-141.
Virtanen, S., 2021. Uncovering dynamic textual topics that explain crime. Royal Society open
science, 8(12), p.210750.

Xu, Y. and et.al., 2021. Identifying key factors associated with ridesplitting adoption rate and
modeling their nonlinear relationships. Transportation Research Part A: Policy and
Practice, 144, pp.170-188.
Yen, C.P. and Hung, T.W., 2021. Achieving Equity with Predictive Policing Algorithms: A
Social Safety Net Perspective. Science and engineering ethics, 27(3), pp.1-16.
Yerpude, P., 2020. Predictive modelling of crime data set using data mining. International
Journal of Data Mining & Knowledge Management Process (IJDKP) Vol, 7.
Yoo, J.S., 2019, December. Crime data warehousing and crime pattern discovery. In Proceedings
of the Second International Conference on Data Science, E-Learning and Information
Systems (pp. 1-6).
Online
https://uofgsotl.blog/2021/07/02/research-onion-sotl-project/