COMPARATIVE STUDY OF DECISION TREE ALGORITHMS FOR DATA ANALYSIS

Authors

  • Ms. Neeru Ahuja
  • Ms. Taruna Mehta Lecturer, D.N P G College, Hisar, Haryana
  • Ms. Urvashi Lecturer, D.N P G College, Hisar, Haryana

Keywords:

Decision Tress,, ID3, SLIQ

Abstract

The Main objective of this paper is to compare the classification algorithms for decision trees for data analysis. Classification problem is important task in data mining. Because today’s databases are rich with hidden information that can be used for making intelligent business decisions. To comprehend that information, classification is a form of data analysis that can be used to extract models describing important data classes or to predict future data trends. Several classification techniques have been proposed over the years e.g., neural networks, genetic algorithms, Naive Bayesian approach, decision trees, nearest-neighbor method etc. In this paper, our attention is restricted to decision tree technique after considering all its advantages compared to other techniques.

References

“A New Approach for Evaluation of Data Mining Techniques”, Moawia Elfaki Yahia1, Murtada Elmukashfi El-taher2, IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 5, September 2010.

“A study on effective mining of association rules from huge database” V.Umarani et. al. / IJCSR International Journal of Computer Science and Research, Vol. 1 Issue 1, 2010.

“K-means v/s K-medoids: A Comparative Study” Shalini S Singh, National Conference on Recent Trends in Engineering & Technology, May 2011. Predicting School Failure Using Data Mining”C. MÁRQUEZ-VERA

"Applications of Data Mining Techniques in Healthcare and Prediction of Heart Attacks” K.Srinivas et al. / (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 02, 2010, 250- 255. en.wikipedia.org/wiki/Data_mining50

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Published

30-06-2014

How to Cite

Ms. Neeru Ahuja, Ms. Taruna Mehta, & Ms. Urvashi. (2014). COMPARATIVE STUDY OF DECISION TREE ALGORITHMS FOR DATA ANALYSIS. International Journal for Research Publication and Seminar, 5(1), 43–50. Retrieved from https://jrps.shodhsagar.com/index.php/j/article/view/37

Issue

Vol. 5 No. 1 (2014): Volume 5 | Issue 1 | Jan-Jun 2014

Section

Original Research Article

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