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

M. Jayakameswaraiah1 , S. Ramakrishna2

Section:Research Paper, Product Type: Journal Paper
Volume-2 , Issue-3 , Page no. 51-54, Mar-2014

Online published on Mar 30, 2014

Copyright © M. Jayakameswaraiah, S. Ramakrishna . 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: M. Jayakameswaraiah, S. Ramakrishna, “Implementation of an Improved ID3 Decision Tree Algorithm in Data Mining System,” International Journal of Computer Sciences and Engineering, Vol.2, Issue.3, pp.51-54, 2014.

MLA Style Citation: M. Jayakameswaraiah, S. Ramakrishna "Implementation of an Improved ID3 Decision Tree Algorithm in Data Mining System." International Journal of Computer Sciences and Engineering 2.3 (2014): 51-54.

APA Style Citation: M. Jayakameswaraiah, S. Ramakrishna, (2014). Implementation of an Improved ID3 Decision Tree Algorithm in Data Mining System. International Journal of Computer Sciences and Engineering, 2(3), 51-54.

BibTex Style Citation:
@article{Jayakameswaraiah_2014,
author = {M. Jayakameswaraiah, S. Ramakrishna},
title = {Implementation of an Improved ID3 Decision Tree Algorithm in Data Mining System},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {3 2014},
volume = {2},
Issue = {3},
month = {3},
year = {2014},
issn = {2347-2693},
pages = {51-54},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=67},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=67
TI - Implementation of an Improved ID3 Decision Tree Algorithm in Data Mining System
T2 - International Journal of Computer Sciences and Engineering
AU - M. Jayakameswaraiah, S. Ramakrishna
PY - 2014
DA - 2014/03/30
PB - IJCSE, Indore, INDIA
SP - 51-54
IS - 3
VL - 2
SN - 2347-2693
ER -

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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.

Key-Words / Index Term

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

References

[1]. 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.
[2]. D. Jiang, Information Theory and Coding [M]: Science and Technology of China University Press, 2001.
[3]. S. F. Chen, Z. Q. Chen, �An Artificial intelligence in knowledge engineering [M]�. Nanjing: Nanjing University Press, 1997.
[4]. M. Zhu, �Data Mining [M]�. Hefei: China University of Science and Technology Press Page No (67-72), 2002.
[5]. 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.
[6]. 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.
[7]. 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.
[8]. S. L. Han, H. Zhang, H. P. Zhou, �correlation function based on decision tree classification algorithm for computer application�, November 2000.
[9]. 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.
[10]. Bharati.M, Ramageri,�Data Mining Techniques and Applications�, Indian journal of Computer Science and Engineering, Volume-01, Issue-04, Page NO (301-305), 2010.
[11]. 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.