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

Zhengyu Ma, Hong Seo Ryoo

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Pages (from-to)44-71
Number of pages28
JournalJournal of Classification
Issue number1
Publication statusPublished - 2021 Apr

Bibliographical note

Funding Information:
This work was supported by research grant awarded to H.S. Ryoo by Samsung Science and Technology Foundation under Project Number SSTF-BA1501-03 and by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant Number: 2017R1D1A1A02018729).

Publisher Copyright:
© 2020, The Classification Society.


  • Classification
  • Rule induction
  • Spherical pattern
  • Supervised learning

ASJC Scopus subject areas

  • Mathematics (miscellaneous)
  • Psychology (miscellaneous)
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences


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