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HDD: a hypercube division-based algorithm for discretisation

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  • Ping Yang
  • Ji-Sheng Li
  • Yong-Xuan Huang

Abstract

Discretisation, as one of the basic data preparation techniques, has played an important role in data mining. This article introduces a new hypercube division-based (HDD) algorithm for supervised discretisation. The algorithm considers the distribution of both class and continuous attributes and the underlying correlation structure in the data set. It tries to find a minimal set of cut points, which divides the continuous attribute space into a finite number of hypercubes, and the objects within each hypercube belong to the same decision class. Finally, tests are performed on seven mix-mode data sets, and the C5.0 algorithm is used to generate classification rules from the discretised data. Compared with the other three well-known discretisation algorithms, the HDD algorithm can generate a better discretisation scheme, which improves the accuracy of classification and reduces the number of classification rules.

Suggested Citation

  • Ping Yang & Ji-Sheng Li & Yong-Xuan Huang, 2011. "HDD: a hypercube division-based algorithm for discretisation," International Journal of Systems Science, Taylor & Francis Journals, vol. 42(4), pages 557-566.
    • Handle: RePEc:taf:tsysxx:v:42:y:2011:i:4:p:557-566
    DOI: 10.1080/00207720903572455
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