A systematic study on data mining methods and applications

Archana R Thakur


Data mining is the practice of extracting concealed, helpful patterns and information from data. It is a novel technology that assists organizations to forecast future trends and actions, permitting them to make practical, knowledge driven decisions. The present work describes the data mining process and how it can assist decision makers to take better decisions. Practically, data mining is very fruitful for large sized organizations with huge amount of data. It also helps to augment the net profit, as a result of correct decisions taken during the right time. This paper presents the various steps taken during the data mining process and how organizations can get better answer queries from huge datasets. It also presents detailed review on data mining methods and applications.



Data mining; classification; clustering; association; prediction.

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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.

DOI: https://doi.org/10.26483/ijarcs.v13i2.6807


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