University Data Mining Report: Stages, Advantages, and Challenges

This report provides a comprehensive overview of data mining, also known as Knowledge Discovery in Databases (KDD). It begins with an introduction to data mining and its role in extracting valuable information from large datasets. The report then delves into the various stages of data mining, including data preparation, data mining techniques (clustering, classification, regression, association rule learning), information expression, and analysis for decision-making. The report further highlights the advantages of data mining, such as its applications in marketing, banking, government, and manufacturing. It also discusses the drawbacks, including privacy and security issues, as well as the potential for inaccurate information and misuse of data. The report concludes by emphasizing the importance of addressing these challenges to maximize the benefits of data mining. The report references several academic sources to support its findings.

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
Discussion:.......................................................................................................................................2
Conclusion:......................................................................................................................................4
References........................................................................................................................................5

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Introduction:
Data mining also known as Knowledge Discovery in Databases (KDD), is defined as the
process of drawing or extracting inherent, earlier unrecognized and conceivably useful
information and knowledge from a large data collection or data sets in databases or data
warehouse. The extraction process is done by using automated data analysis techniques, which
sorts data sets to identify patterns to establish relationships. These are used by the data mining
toll to predict future trends. The necessary difference between traditional data analysis such as
query, online application of analysis and reporting with data mining is that data mining excavate
information and reveal knowledge on ground of indistinct presumption (Sahu, Shrma &
Gondhalakar, 2011).
Discussion:
Data mining stages: They are as follows.
1) Preparation of data (or pre-processing of data): Data preparation is data manipulation into
suitable form for further processing and analysis. The process of data preparation consists of
various tasks such as collecting, integrating, structuring, translating and validating data and those
tasks cannot be fully automated and many of them are tedious and time-consuming. The main
reason for data preparation is to ensure that the required prepared information for analysis must
be consistent and accurate. Data preparation is required for successful data mining (Provost &
Fawcett, 2013).
2) Data Mining: This stage is the nucleus of the overall process, which primarily utilises the
gathered techniques of mining as well as tools for dealing with the data. This stage involves the
following activities.
Data mining method collection

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 Clustering: It is a technique of data mining that places data elements into connected groups
without having group definitions prior knowledge.
 Classification: It generalizes the known structure and apply it to new data.
 Regression: It takes the numerical dataset and builds the mathematical formula as per the
results.
 Association rule learning (or market basket analysis): It searches and discover the
relationships between variables (Witten et al., 2016).
Data mining algorithm collection: The data mining algorithm are divided into components
such as model or pattern structure, score function, searching procedure, strategy of data
management.
3) Information expression: It is the method of using the technology for visualization and
knowledge information expression.
4) Analysis and decision-making: Data mining’s final objective is to help in decision-making
process as decision-makers analyses data mining outcomes and modify the methods of decision-
making by incorporating with real situation (Romero & Ventura, 2013).
Advantages of data mining: They are as follows.
 Marketing companies can use data mining to develop models for predicting about who will
respond to new marketing campaign. The prediction from the campaign helps the marketers
to approach targeted customers for selling profitable products.
 Banks can use data mining to detect fraudulent transactions of credit card to prevent losses of
the credit card owners.
 Government agencies can use data mining to analyse the financial transaction records and
build pattern that can criminal activity or money laundering.

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 Manufacturers can use data mining technique on functional data to detect malfunctioning
machinery or device and find out best control guidelines.
 The tools and techniques can be used to handle database security problems such as detection
of intrusion and database auditing (Sahu, Shrma & Gondhalakar, 2011).
Drawbacks of data mining:
1) Issues of privacy: Data mining process is effectively based on data preparation task that can
disclose information or patterns that may compromise privacy and confidentiality accountability.
This occurs, commonly through data aggregation. In data aggregation, data are compiled or
accumulated from various sources and put together for data analysis purpose. After compilation,
anybody who mines data has access to the recently accumulated data set can identify particular
individuals particularly when data were anonymous in original. This can put threat to an
individual’s privacy (Wu et al., 2014).
2) Issues of security: Application of data mining techniques can cause security problems. Data
mining makes it possible for unethical people to pick up and gather a significant amount of
information about individuals from the routine business transactions that is associated with his /
her buying habits and preferences (Jaseena & David, 2014).
3) Inaccurate information / misuse of information: It is notable that the tools and technique
of data mining may not always be perfectly accurate and thus if inaccurate information is
provided for decision-making then it will result serious consequence. In addition, there are
chances of misuse of information that is collected through data mining. Unethical people can
exploit this information to assailable people (Sahu, Shrma & Gondhalakar, 2011).

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Conclusion:
From the above discussion, it draws conclusion the technology of data mining is an
application oriented one which prepares, integrates, validates, transforms and analyses data
besides searching and querying in order to provide appropriate solution to problem of real-life,
find the connection between events as well as to forecast future activities. The discussion also
depicts about how data mining provide benefits to society, government and business. Further, the
discussion also shows that issues such as security, privacy and misuse of data and information
can pose big problem if they are not addressed appropriately in time.
References:

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Jaseena, K. U., & David, J. M. (2014). Issues, challenges, and solutions: Big data
mining. Computer Science & Information Technology (CS & IT), 131-140.
Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data
mining and data-analytic thinking. " O'Reilly Media, Inc.".
Romero, C., & Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews:
Data Mining and Knowledge Discovery, 3(1), 12-27.
Sahu, H., Shrma, S., & Gondhalakar, S. (2011). A brief overview on data mining
survey. International Journal of Computer Technology and Electronics Engineering
(IJCTEE) Volume, 1, 114-121.
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine
learning tools and techniques. Morgan Kaufmann.
Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE transactions
on knowledge and data engineering, 26(1), 97-107.