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题名: A Discretization Algorithm Based on Class-Attribute Contingency Coefficient
作者: Cheng-Jung Tsai;Chien-I. Lee;Wei-Pang Yang
贡献者: 數學系
关键词: Data mining;Classification;Decision tree;Discretization;Contingency coefficient
日期: 2008-02
上传时间: 2011-05-10T06:29:08Z
出版者: Elsevier Science
摘要: 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.
關聯: Information Sciences, 178(3):714-731
显示于类别:[數學系] 期刊論文





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