Revised entropy clustering analysis with features selection

Authors:
Ching-Hsue Cheng;Jing-Rong Chang;I-Ni Lei
Affiliations:
Department of Information Management, National Yunlin University of Science and Technology, Yunlin, Taiwan;Department of Information Management, National Yunlin University of Science and Technology, Yunlin, Taiwan;Graduate School of Resources Management, National Defense Management College, Taipei, Taiwan
Venue:
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Year:
2005

Citing 4
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Abstract

Clustering analysis is used to analyze the clustering phenomenon occurred to the data structure. However, there are some problems when the decision maker attempts to use clustering analysis. For solving these existing problems, this paper proposes a revised Entropy Clustering Analysis method requiring no prior setting of clusters, which is based on the mean distance between the data points and the cluster center. Through using several experiments and comparing different clustering analysis methods with proposed method, the results show that the proposed clustering method could achieve reasonable clustering effect. The experiment also proves that using the attributes with high correlation coefficient in clustering can achieve higher clustering accuracies.