Heuristic approximation methods for principal points for binary distributions

Haruka Yamashita, Hideo Suzuki

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

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
Volume65
Issue number2
Publication statusPublished - 2014
Externally publishedYes

Fingerprint

Principal Points
Heuristic methods
Heuristic Method
Approximation Methods
Binary
Heuristics
Approximation

Keywords

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

ASJC Scopus subject areas

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

Cite this

Heuristic approximation methods for principal points for binary distributions. / Yamashita, Haruka; Suzuki, Hideo.

In: Journal of Japan Industrial Management Association, Vol. 65, No. 2, 2014, p. 131-141.

Research output: Contribution to journalArticle

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