A Comprehensive Review on Data Mining Techniques and Applications

Authors

  • A Thakur School of Computer Science & IT, Devi Ahilya Vishwavidyalaya, Indore, Madhya Pradesh, India

Keywords:

Data Mining, Classification, Clustering, Association Rule, Neural Network

Abstract

Data mining is the study of mining concealed, helpful patterns and information from data. It is a new technology that helps organizations to estimate future trends and actions, allowing them to make real-world, knowledge driven decisions. The current work discusses the data mining process and how it can help the decision makers to opt for better decisions. Practically, data mining is very productive for large sized organizations with enormous amount of data. It also aids to augment the net profit, as a consequence of right decisions taken during the exact time. This paper presents the different steps taken during the data mining process and how organizations can have better answer to the queries from huge datasets. It also presents a systematic review on data mining techniques and applications.

References

J. Han, M. Kamber, and J. Pei, “Data Mining Concepts and Techniques”, Third edition The Morgan Kaufmann Series in Data Management Systems Morgan Kaufmann Publishers, July 2011.

R.R Kabra, and R.S. Bichkar, “Performance Prediction of Engineering Students using Decision Tree”, International Journal of computer Applications, Vol.36, Issue.11, pp.8- 12, December 2011.

B.M. Ramageri, “Data Mining Techniques and Applications”, Indian Journal of Computer Science and Engineering Vol.1 No.4, pp.301-305, 2010.

M.H. Dunham, “Data Mining, Introductory and Advanced Topics”, Pearson Education, 2014.

G. Parker, “Data Mining: Modules in emerging fields, CD- ROM”, Vol.7, 2004.

K.E. DiCerbo, and K. Kidwai, “Detecting player goals from game log files,” in Poster presented at the Sixth International Conference on Educational Data Mining (Memphis, TN), 2013.

M. Rafiuzzaman, “Forecasting Chaotic Stock Market Data using Time Series Data Mining”, International journal of computer application (0975-8887) Volume 101- Issue 10, September 2014.

B. Xu, M. Recker, X. Qi, N. Flann, and L. Ye, “Clustering educational digital library usage data: a comparison of latent class analysis and k-means algorithms. J. Educ. Data Mining 5, pp.38–68, 2013.

M. Venkatadri, and L.C. Reddy, “A comparative study on decision tree classification algorithm in data mining”, International Journal of Computer Applications in Engineering, Technology and Sciences, Vol.2, Issue.2, pp. 24-29, 2010.

Downloads

Published

2025-11-12

How to Cite

[1]
A. Thakur, “A Comprehensive Review on Data Mining Techniques and Applications”, Int. J. Comp. Sci. Eng., vol. 6, no. 1, pp. 417–421, Nov. 2025.

Issue

Vol. 6 No. 1 (2018): IJCSE January Ediion

Section

Review Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.