doi: 10.7763/IJMLC.2012.V2.164
An Efficient Classification Approach for Data Mining
Hem Jyotsana Parashar2
,
Singh Vijendra1 ,
Nisha Vasudeva2
- 1Department of Computer Science and Engineering, Faculty of Engineering and Technology, Mody Institute of Technology and Science, Lakshmangarh, Sikar, Rajasthan, India.
- 2Department of Computer Science Engineering, Arya college of Engineering and IT, Kukas, Jaipur Rajasthan, India.
Abstract
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. In this paper, the shortcoming of ID3's inclining to choose attributes with many values is discussed, and then a new decision tree algorithm which is improved version of ID3. In our proposed algorithm attributes are divided into groups and then we apply the selection measure 5 for these groups. If information gain is not good then again divide attributes values into groups. These steps are done until we get good classification/misclassification ratio. The proposed algorithms classify the data sets more accurately and efficiently.
Keywords
- Classification
- decision tree
- knowledge engineering
- data mining
- supervised learning
How to Cite
Hem Jyotsana Parashar, Singh Vijendra, and Nisha Vasudeva, "An Efficient Classification Approach for Data Mining," International Journal of Machine Learning, vol. 2, no. 4, pp. 446-448, 2012. https://doi.org/10.7763/IJMLC.2012.V2.164
Copyright & License
Copyright © 2012 by the authors. 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 (CC BY 4.0).