A discretization algorithm based on Class-Attribute Contingency Coefficient

Cheng Jung Tsai, Chien I. Lee, Wei Pang Yang

Research output: Contribution to journalArticlepeer-review

145 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)714-731
Number of pages18
JournalInformation Sciences
Issue number3
Publication statusPublished - 2008 Feb 1

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

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