Heuristic approximation methods for principal points for binary distributions

Haruka Yamashita, Hideo Suzuki

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


The analysis of binary (0 or 1) data requires an analysis method whose objects are realizations. Yamashita and Suzuki (to appear) proposed principal points for binary distributions based on the concept of principal points, defined by Flury (1990). Ideally, when we search for the binary principal points, all combinations of the k-principal points should be considered; however, this problem cannot be solved in a straightforward manner because the number of combinations increases exponentially when the number of the variables increases. In this paper, we propose three heuristic methods for approximating principal points for binary distributions. The results indicate that our method enables us to find approximated principal points and summarize a binary distribution using the points.

Original languageEnglish
Pages (from-to)131-141
Number of pages11
JournalJournal of Japan Industrial Management Association
Issue number2
Publication statusPublished - 2014 Jan 1


  • Binary distributions
  • Data analysis
  • Heuristic approximation method
  • K-means algorithm
  • Principal points

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering
  • Applied Mathematics


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