Fuzzy c-means algorithms for data with tolerance based on opposite criterions

Kanzawa Yuchi, Endo Yasunori, Miyamoto Sadaaki

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

In this paper, two new clustering algorithms are proposed for the data with some errors. In any of these algorithms, the error is interpreted as one of decision variables - called "tolerance" - of a certain optimization problem like the previously proposed algorithm, but the tolerance is determined based on the opposite criterion to its corresponding previously proposed one. Applying our each algorithm together with its corresponding previously proposed one, a reliability of the clustering result is discussed. Through some numerical experiments, the validity of this paper is discussed.

Original languageEnglish
Pages (from-to)2194-2202
Number of pages9
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
VolumeE90-A
Issue number10
DOIs
Publication statusPublished - 2007 Oct

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Clustering algorithms
Experiments

Keywords

  • Clustering
  • Fuzzy c-means
  • Reliability of the clustering result
  • Tolerance

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Graphics and Computer-Aided Design
  • Applied Mathematics
  • Signal Processing

Cite this

Fuzzy c-means algorithms for data with tolerance based on opposite criterions. / Yuchi, Kanzawa; Yasunori, Endo; Sadaaki, Miyamoto.

In: IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, Vol. E90-A, No. 10, 10.2007, p. 2194-2202.

Research output: Contribution to journalArticle

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