Implementation of an Improved ID3 Decision Tree Algorithm in Data Mining System

Authors

  • M Jayakameswaraiah Dept.of Computer Science, Sri Venkateswara University, Tirupati, India
  • S Ramakrishna Dept.of Computer Science, Sri Venkateswara University, Tirupati, India

Keywords:

Data Mining, Decision tree, ID3Algorithm, Association Function (AF), Classification

Abstract

Inductive learning is the learning that is based on induction. In inductive learning Decision tree algorithms are very famous. For the appropriate classification of the objects with the given attributes inductive methods use these algorithms basically. Decision tree is an important method for both induction research and data mining, which is mainly used for model classification and prediction. ID3 algorithm is the most widely used algorithm in the decision tree so far. Through illustrating on the basic ideas of decision tree in data mining, in this paper, the shortcoming of ID3�s inclining to choose attributes with many values is discussed, and then a new decision tree algorithm combining ID3 and Association Function (AF) is presented. The experiment results show that the proposed algorithm can overcome ID3�s shortcoming effectively and get more reasonable and effective rules. The algorithm is implemented in the java language.

References

I. H. Witten, E. Frank, “Data Mining Practical Machine Learning Tools and Techniques”, San Francisco: Morgan Kaufmann Publishers. China Machine Press, second edition ISBN 0-12-088407-0,560 pp, 2005.

D. Jiang, Information Theory and Coding [M]: Science and Technology of China University Press, 2001.

S. F. Chen, Z. Q. Chen, “An Artificial intelligence in knowledge engineering [M]”. Nanjing: Nanjing University Press, 1997.

M. Zhu, “Data Mining [M]”. Hefei: China University of Science and Technology Press Page No (67-72), 2002.

A. P. Engelbrecht., “A new pruning heuristic based on variance analysis of sensitivity information [J]”. IEEE Trans on Neural Networks, Volume-12 Issue-06, Page No (1386-1399), November 2001.

N. Kwad, C. H. Choi, “Input feature selection for classification problem [J]”, IEEE Trans on Neural Networks, Volume-13 Issue-01, Page No (143- 159), 2002.

X. J. Li, P. Wang, “Rule extraction based on data dimensionality reduction using RBF neural networks”. ICON IP2001 Proceedings, 8th International Conference on Neural Information Processing [C]. Shanghai, China, Page No (149- 153), 2001.

S. L. Han, H. Zhang, H. P. Zhou, “correlation function based on decision tree classification algorithm for computer application”, November 2000.

S. Y. Zhang, Z. Y. Zhu, “Study on decision tree algorithm based on autocorrelation function”. Systems Engineering and Electronic Volume-27 Issue-07 Jul. 2005.

Bharati.M, Ramageri,”Data Mining Techniques and Applications”, Indian journal of Computer Science and Engineering, Volume-01, Issue-04, Page NO (301-305), 2010.

Kalpesh Adhatrao, Aditya Gaykar, Amiraj Dhawan, Rohit Jha and Vipul Honrao,”Predicting,“Students Performance Using ID3 and C4.5 classification Algorithms”, International journal Data mining and knowledge management process,Volume-03,Issue-05,September 2013.

Downloads

Published

2014-03-31

How to Cite

[1]
M. Jayakameswaraiah and S. Ramakrishna, “Implementation of an Improved ID3 Decision Tree Algorithm in Data Mining System”, Int. J. Comp. Sci. Eng., vol. 2, no. 3, pp. 51–54, Mar. 2014.

Issue

Vol. 2 No. 3 (2014): IJCSE March Edition

Section

Research 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.