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Spherical Classification of Data, a New Rule-Based Learning Method

Author

Listed:
  • Zhengyu Ma

    (Korea University)

  • Hong Seo Ryoo

    (Korea University)

Abstract

This paper presents a new rule-based classification method that partitions data under analysis into spherical patterns. The forte of the method is twofold. One, it exploits the efficiency of distance metric-based clustering to fast collect similar data into spherical patterns. The other, spherical patterns are each a trait shared among one type of data only, hence are built for classification of new data. Numerical studies with public machine learning datasets from Lichman (2013), in comparison with well-established classification methods from Boros et al. (IEEE Transactions on Knowledge and Data Engineering, 12, 292–306, 2000) and Waikato Environment for Knowledge Analysis ( http://www.cs.waikato.ac.nz/ml/weka/ ), demonstrate the aforementioned utilities of the new method well.

Suggested Citation

  • Zhengyu Ma & Hong Seo Ryoo, 2021. "Spherical Classification of Data, a New Rule-Based Learning Method," Journal of Classification, Springer;The Classification Society, vol. 38(1), pages 44-71, April.
    • Handle: RePEc:spr:jclass:v:38:y:2021:i:1:d:10.1007_s00357-019-09355-z
    DOI: 10.1007/s00357-019-09355-z
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    References listed on IDEAS

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