Short Literature Review on AI and Data Mining Techniques

This literature review explores the advancements and applications of Artificial Intelligence (AI) and Data Mining technologies. It begins by defining AI and Data Mining, highlighting their roles in various sectors and introduces the statistical techniques used in AI, including problem framing, understanding, and data cleaning. The review then details data mining techniques like association, classification, clustering, prediction, and sequential patterns, comparing statistical techniques and data mining, emphasizing their differences in information processing and problem-solving approaches. It also presents a literature review on the application and concept of both techniques, discussing their increasing importance and potential future impacts. The review concludes by summarizing the advantages of statistical techniques while acknowledging the potential threats posed by AI's increasing capabilities, referencing several books, journals and online sources.

Short literature review on
novel research in area of
computer science related to
Artificial Intelligence
Technologies

TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................1
Methods and techniques supporting artificial intelligence and Data Mining..................................1
Comparative analysis of AI and Data Mining.................................................................................4
Literature review on application and concept of AI and Data Mining............................................5
CONCLUSION................................................................................................................................6
REFERENCES................................................................................................................................7

INTRODUCTION
Artificial intelligence (AI) is defined as an advanced computer science technology in
which intelligent machines are developed which are capable to demonstrate the natural
intelligence, similar to human beings or other living organisms (Pan, 2016). Data mining is a
practice to analyse large size of data which is pre exists in the record to generate new data and
information. AI is playing significant role in healthcare, manufacturing and in aspects which are
beyond human imagination or reach. The Statical techniques are used in the AI to perform the
different operation based on the mathematical based solution. The study will analyse the
techniques which supports the AI and its applications. It will also provide a comparative
evaluation of AI and other recent technologies. The data mining technology is used to analysis
the pre exist to generate the new out comes. AI is problem solving technology which is based on
the predefined process and information. The AI works on the predefined algorithm and gain the
information form data analysing process.
Methods and techniques supporting artificial intelligence and Data Mining
Data Mining
Data mining is a software which is used to examine the pre-existed data to generate the
result and find the patterns in the data for better classified result. The various techniques used in
the Data Mining are-
1. Association- This technique is to collect the information which are of the same type. In
this the data of same nature is scanned and then collected together for more convenience.
2. Classification- It is based on the arranging the data of the same type in the classified
manner. This technique scans the data and separate the same type of data together in
classified way. This used in the businesses to classify the information.
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3. Clustering- This technique is used in the production department of the organisation to
separate the product as the predefined information. This method uses the physical
scanners to take the input and then process the information to perform the action.
4. Prediction- This method is mostly used by the scientists and the business annalist to
predict the future result. In this technology the collected data is scanned and the patterns
are evaluated. This scanned patterns are used to scan current situation data and predict the
result.
5. Sequential Pattern- These techniques are used to find the patterns in the data and judge
the current situation. To find the trends and business period.
6. Decision Tree- These techniques are used to generate the decision via the machine
learning. In this process the data is analysed and as per the information patterns the
technique generate the most suitable result for the data (Aggarwal, 2015).
Statistical Techniques
Since other techniques have predefined and installed software and algorithm they are not
required to use logics or decision at their own. However contrary to this AI devices first try to
discover the possible solution of given problem and then it is executed. The techniques which
supports AI are largely based upon mathematical and statistical models so that it can show
human intelligence. The most popular methods which are used for the development and
implementation of AI are as follows:
Problem Framing
Problem framing of AI based on the designing of AI numbers of nodes are used which
are capable identify data patterns. It uses methods like supervised and unsupervised learning,
reinforced learning for the pattern recognition (Li & Du, 2017). Neural network techniques help
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AI to make decisions on the basis of logical reasoning and identification. This helps the AI to
calculate the solving method.
Problem Understanding
It is machine algorithm which is based on supervised learning method so that problems
can be classified and regression challenges can be overcome. The ability of AI to solve problems
at their own is achieved by this technique by understanding the problem and its type. For the
decision makes by AI are based on this process. This technique is also used by AI for performing
functions such as image and text recognition as well as face recognition.
Data Cleaning
The ability of AI to clean the data which is not useful to solve data problems quickly then
this method is provided to keep the AI free from rubbish data. This algorithm aims to reduce the
west data so that minimum number of alternatives are present and fast decisions can be made
because it needs to process small amount of data. It also assists AI machines to recognise the
mistakes and to learn from them so that similar to human being experience and learnings can be
continuously increase.
As per the above techniques the artificial intelligence use these techniques in collective
manner to develop an artificial intelligence device to reduce the work load of human. For
example in the large scale organisation the artificial widely used in the production line and
assembly line. These devices usually known as industrial robots. This type of robots use both
data mining and statical techniques to perform the required action for the production and
assembly. In the mobile assembly the procedure need high precision to make the electrical
connection proper for required functioning of cellphone. This is how the artificial intelligence is
now used for high precision works.
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(Source: Introduction to Natural Language Processing. (2018))
Comparative analysis of statistical Technique and Data Mining
Artificial Intelligence work is based on the algorithm structure which is designed to
analysis the data gain the information and then implement this learning in problem solving. The
statistical techniques are the techniques which are used on the base of the interface with the
humans and physical experiences and store the information collectively from each operation and
further use this information to solve the different task. But when it comes to Data Mining, Data
Mining scans the available data and process through different algorithms and gives the unique
solution or result for current data related query. The main difference of the Artificial Intelligence
and Data Mining is the information screening with collective storing and other is the problem
solving skills.
Statistical Techniques Data Mining
It is the basic method which is consists of the It is the advance way to scan the information
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Illustration 1: Natural language processing

mathematical formulas and technique to scan
and analyse the raw data.
and sort it for unique report.
The of statistical techniques works with
mathematical formulas to calculate the further
result.
Data mining works on the patterns and
sequences of the data to find the result.
Statistical Techniques only follows the
algorithm to implement the mathematical
equation and formulas on the data based on the
required action.
Data Mining is based on the learning
algorithms, which learns from the situation and
data to gives the calculated result.
Statistical Techniques is process to examine
data with the formulas to produce the result.
Data Mining analyse the data and store the
information in collective manner to solve the
problem related to the data.
Statistical Techniques process don't have self
intelligence it only follows the algorithms and
mathematical processes.
Data Mining follows the algorithms which are
more complex and design to learn. And follow
both given commands and self generated
commands.
It is simple to understand. Less complexity. The complexity is higher in data processing.
Literature review on application and concept of Statistical Techniques and
Data Mining
Contrary to this Lu & et.al., (2018) asserted that there is high need when for assuring the
developments we must encourage Statistical Techniques but its reliability must not be neglected.
The increasing role of Statistical Techniques is giving the result data as per the formulas this may
lead to the different data than the reality. The statistical techniques are based on the mathematical
formulas to study the raw data to produce the result In such situation in future it may become
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impossible to stop or manage Statistical Techniques because they will more powerful than
human.
As per the views of Tan, P. N. (2018) the data mining is the basic part of the Artificial
Intelligence. The techniques of Data Mining are the basic principle for the artificial intelligence.
The data processing method used in the Artificial Intelligence is from Data Mining and inspired
from it. The same techniques which are used in the data mining are implemented in the AI. The
Ai is the advanced version of the data mining with the additional features of advanced decision
making and problem solving technique.
CONCLUSION
The study of Data mining shows that the process is less complex and easy to understand
and required machinery is less than the AI. But it has some limitation in the operation. From the
above study it can be concluded that statistical techniques are better than Data Mining and can be
considered as one of the most critical developed technology which can take the superior decision
and can conduct the decision on its own which make more effective cause this are based on the
mathematical solution. However, the fact cannot be denied that as it is approaching to
applications which are beyond human capabilities gradually it is also creating a threat on
humanity and substitution of human beings.
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REFERENCES
Books and Journals
Aggarwal, C. C. (2015). Data mining: the textbook. Springer.
El Kadiri, S. & et.al., (2016). Current trends on ICT technologies for enterprise information
systems. Computers in Industry. 79. 14-33.
Fortunati, L. (2017). The human body: Natural and artificial technology. In Machines that
become us (pp. 71-87). Routledge.
Li, D., & Du, Y. (2017). Artificial intelligence with uncertainty. CRC press.
Lu, H. & et.al., (2018). Brain intelligence: go beyond artificial intelligence. Mobile Networks and
Applications. 23(2). 368-375.
Pan, Y. (2016). Heading toward artificial intelligence 2.0. Engineering. 2(4). 409-413.
Posada, J. & et.al., (2015). Visual computing as a key enabling technology for industrie 4.0 and
industrial internet. IEEE computer graphics and applications. 35(2). 26-40.
Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach. Malaysia;
Pearson Education Limited,
Tan, P. N. (2018). Introduction to data mining. Pearson Education India.
Online
Fourtane., S., (2018). 5 Technology trends to watch in 2019. [Online]. Accessed through
<https://interestingengineering.com/5-technology-trends-to-watch-in-2019>
Ghosh., P., (2018). The Future of Machine Learning and Artificial Intelligence. [Online].
Accessed through <https://www.dataversity.net/future-machine-learning-artificial-
intelligence/>
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Introduction to Natural Language Processing, (2018). [Online]. Accessed through
<https://medium.com/greyatom/introduction-to-natural-language-processing-
78baac3c602b>
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