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A Comprehensive Review on Data Mining Techniques and Applications

A. Thakur1

Section:Review Paper, Product Type: Journal Paper
Volume-6 , Issue-1 , Page no. 417-420, Jan-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i1.417420

Online published on Jan 31, 2018

Copyright © A. Thakur . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: A. Thakur, “A Comprehensive Review on Data Mining Techniques and Applications,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.417-420, 2018.

MLA Style Citation: A. Thakur "A Comprehensive Review on Data Mining Techniques and Applications." International Journal of Computer Sciences and Engineering 6.1 (2018): 417-420.

APA Style Citation: A. Thakur, (2018). A Comprehensive Review on Data Mining Techniques and Applications. International Journal of Computer Sciences and Engineering, 6(1), 417-420.

BibTex Style Citation:
@article{Thakur_2018,
author = {A. Thakur},
title = {A Comprehensive Review on Data Mining Techniques and Applications},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {1 2018},
volume = {6},
Issue = {1},
month = {1},
year = {2018},
issn = {2347-2693},
pages = {417-420},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5623},
doi = {https://doi.org/10.26438/ijcse/v6i1.417420}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i1.417420}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5623
TI - A Comprehensive Review on Data Mining Techniques and Applications
T2 - International Journal of Computer Sciences and Engineering
AU - A. Thakur
PY - 2018
DA - 2018/01/31
PB - IJCSE, Indore, INDIA
SP - 417-420
IS - 1
VL - 6
SN - 2347-2693
ER -

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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.

Key-Words / Index Term

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

References

[1] 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.
[2] 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.
[3] B.M. Ramageri, “Data Mining Techniques and Applications”, Indian Journal of Computer Science and Engineering Vol.1 No.4, pp.301-305, 2010.
[4] M.H. Dunham, “Data Mining, Introductory and Advanced Topics”, Pearson Education, 2014.
[5] G. Parker, “Data Mining: Modules in emerging fields, CD- ROM”, Vol.7, 2004.
[6] 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.
[7] 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.
[8] 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.
[9] 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.