A discretization algorithm based on Class-Attribute Contingency Coefficient

Authors:
Cheng-Jung Tsai;Chien-I. Lee;Wei-Pang Yang
Affiliations:
Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan, ROC;Department of Information and Learning Technology, National University of Tainan, Tainan, Taiwan, ROC;Department of Information Management, National DongHwa University, Hualien, Taiwan, ROC
Venue:
Information Sciences: an International Journal
Year:
2008

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Abstract

Discretization algorithms have played an important role in data mining and knowledge discovery. They not only produce a concise summarization of continuous attributes to help the experts understand the data more easily, but also make learning more accurate and faster. In this paper, we propose a static, global, incremental, supervised and top-down discretization algorithm based on Class-Attribute Contingency Coefficient. Empirical evaluation of seven discretization algorithms on 13 real datasets and four artificial datasets showed that the proposed algorithm could generate a better discretization scheme that improved the accuracy of classification. As to the execution time of discretization, the number of generated rules, and the training time of C5.0, our approach also achieved promising results.