Data Mining: Milestones, Impact and Future Scope

This article discusses the milestones, impact and future scope of data mining. It covers various sectors where data mining is utilized, such as education, health, public, telecommunication, construction, and science and engineering sectors.

Running head: DATA MINING
DATA MINING
Name of the Student
Name of the University
Author note

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Table of Contents
Introduction:....................................................................................................................................2
Millstone of this innovation:............................................................................................................2
Impact on different sectors..............................................................................................................3
Future Scope....................................................................................................................................5
Conclusion:......................................................................................................................................7

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Introduction:
As the adoption of technology is growing rapidly, number of smart devices are also
increasing all over the world. This devices are used by enormous amount of people to transfer
data, automate tasks and communicating with different devices. As this applications of such
technologies are growing, the total data usages is also increasing. Devices such as phones,
computers are constantly generating data every day. Generally, huge amount of raw data are
stored into a physical database or cloud. In both cases, data must be stored, organized,
summarized, analyzed and synthesized for optimal decision-making purposes. Useful as well as
desiring the information is very difficult to extract with the large number of data available.
Data mining is basically a statistical application, the methods of data mining are
somewhat different from the statistical methods. Even though data mining has gain popularity
around this century. The data mining has evolved many years before the evolvement of
Moneyball and Edward Snowden (Witten et al. 206). The following shows the major milestones
about how the data mining evolved with blended data science as well as big data.
Millstone of this innovation:
In 1763, Thomas Bayes’ published a paper associated with the relating probability. This is
known as bay’s theorem as considered as a fundamental of data mining because it helps to
understand the complex realities that are based on the estimated probabilities.
In the year 1805, Carl Friedrich Gauss as well as Adrien-Marie Legendre construct regression
for defining body orbits around the Sun, planets as well as comets. For regression analysis,
estimating relationship among the variables is done. It is still considered as a key too in data
mining.

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In 1936, Alan Turing published a paper which provides certain idea of a universal machine
which is capable of computing like modern day computers.
1943 Warren McCulloch and Walter Pitts were the first to create a conceptual model of a neural
network. This network is capable of receive inputs, process inputs and generate output.
In 1970, Moderate database management system enables the possibility to store and
query petabytes and terabytes of data. This type of warehouse enable analytical way of viewing
the data (Larose and Larose 2014).
In 1980, HNC first introduced the phrase “database mining”. The purpose of the workstation was
to build a neural network.
1989 The term “Knowledge Discovery in Databases” (KDD) is coined by Gregory Piatetsky-
Shapiro.
Present (2018) finally, one of the most active techniques being explored today is Deep Learning.
Impact on different sectors
Nowadays, data mining is utilized in various organization including education sector, health
sector, public sector, telecommunication sector, construction sector and science and engineering
sector.
Education Sector: Data mining is used in various studies in the education sector including:
 Determining the relationships between the socioeconomic level of students and the level
of academic learning.

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 Determining the relationship between academic success and participation in
extracurricular activities of university students.
 Preventing students from failing and determining the factors that affect success.
 Determining the profiles and preferences of students entering the university entrance
exam.
 Choosing a profession according to the demographic and personal characteristics.
Health Sector:
 to prevent corruption in hospital expenditures
 to determine the high risk factors in surgeries
 to classify the patient data according to factors such as age, gender, race and treatment,
 to set the success of treatment methods applied in the hospital,
 to estimate the resource use and patient numbers in hospital
 to determine the treatment method to be applied to the disease
Telecommunication Sector
 to identify the characteristics of customers who need special action as suspension or
deactivation
 to determine user templates for social network usage
 to determine the future movements of mobile users,
 creating the preference of university department, determining the factors that influence
the preference order of new enrolled students
 determining the status of students’ pass and fail

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Public Sector:
 To determine which offenders are likely to commit crimes in terms of safety.
 to forecast the future of public investment, analyze the data in defense industry
 to classify public expenditures, plan the correct use of resources
 to measure performance of employees, manage business processes
 for measuring the performance of employees, manage business processes
 for measuring the performance of employees, manage the business processes
Construction Sector
 to determine the location of data mining method in construction management
 to determine which offenders are likely to commit crimes in terms of safety
 to determine the relationship of leadership-motivation between the chief and the worker
 to forecast the future of public investment, analyze the data in defense industry
 to classify public expenditures, plan the correct use of resources
 to measure performance of employees, manage the business processes
 to estimate population, forecast the weather, determine new job opportunities
Future Scope
Data mining is used for forming similarities for searching the value of business information in
the large database. Data mining mainly provides an optimal solution to analyze data in order to
collect and manage data. It also can generate new business opportunity in near future which are
followed:

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Collective or distributed Mining: Researchers are heavily focusing on the collective and
distributed data mining as it is getting as high amount of attention. Most of the researchers are
focused on the physical database and data warehouse to store and collect information more
effectively (Wu et al. 2014). The problem occurs when warehouses are situated in different area.
It is hard to collect information from different location. This is commonly known as Distributed
data mining. As for instance, data from different farms consist of different branches of a
corporation, it is very difficult to analyze as well as extract desired data from this data source.
The concept of DDM uses various types of approaches that are used traditionally for analyzing
the combination of the local data analysis (Romero and Ventura 2013). To perform the local data
analysis, there should be data models generated as well as combining the data models from
various data sites for developing the global market.
Ubiquitous data mining: The advantage of using mobile phones, laptops as well palmtops for
generating the using of access for the large quality of the data that are possible. For analyzing the
data in an advanced way, the data are used for extracting the information. For accessing as well
as analyzing the data from the device mainly offers many challenges. For example, the UDM
provides extra cost because of computing, communication, as well and security (Fan and Bifet
2013). The main object of UDM is mining the data for minimizing cost of the device. The
second challenge that is provided by the UDM is the interaction between the human and the
computer. There are visualize patterns involving clusters, and classifiers that are associated. With
the small areas of display, there are serious challenges that are offered for interactive
environment of data mining (Freitas 2013). The management of data in mobile environment also
creates a big challenge. The main issues that are considered mainly includes UDM, algorithm of

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the distributed as well as mobile communication. There are also markup languages as well as
various representation of the techniques that are available for UDM.
Hypertext and the Hypermedia data mining: Hypertext and the hypermedia data mining are
categorized as the data mining that includes many hyperlinks, other hypertext forms of
information as well as the text mark up. This data mining is mainly related to the web mining as
well as the multimedia mining. In case of supervised learning or classification, one process
mainly starts by providing reviewing the training data where the items are marked for being part
of the class or the group. The data is basic that helps to construct the algorithm (Demšar et al.
2013). There can be optimal solution for data mining that helps in supervised learning and also
helps to analyze the social network.
Conclusion:
Data mining is basically a statistical application, the methods of data mining are
somewhat different from the statistical methods. The data mining provides an optimal solution to
analyze data in order to collect and manage data. Nowadays, it is widely adopted by various
organizations in order to taking advantages of automation (Baker and Inventado 2014).
Typically. It tasks huge expenses and time to analyze data and extracting desired information.
Data mining is very useful and adopted by medical sectors, health sector, public sector,
telecommunication sector, construction sector and science and engineering sector (Braha 2013).
Since the early development of the miming, it provides multiple opportunities and the list is still
growing. Data mining provides some key advantages over the typical neural network. In this
paper, basic description of the data mining is illustrated along with the important milestones of
deployment. Later the possible future scopes are illustrated along with the impact of data mining
on various organization.

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Reference:
Witten, I.H., Frank, E., Hall, M.A. and Pal, C.J., 2016. Data Mining: Practical machine learning
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