Journal
INFORMATION SCIENCES
Volume 178, Issue 3, Pages 714-731Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2007.09.004
Keywords
data mining; classification; decision tree; discretization; Contingency Coefficient
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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. (c) 2007 Elsevier Inc. All rights reserved.
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