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

Kanzawa Yuchi; Endo Yasunori; Miyamoto Sadaaki

doi:10.1093/ietfec/e90-a.10.2194

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

Kanzawa Yuchi, Endo Yasunori, Miyamoto Sadaaki

Research output: Contribution to journal › Article › peer-review

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 language	English
Pages (from-to)	2194-2202
Number of pages	9
Journal	IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Volume	E90-A
Issue number	10
DOIs	https://doi.org/10.1093/ietfec/e90-a.10.2194
Publication status	Published - 2007 Oct

Keywords

Clustering
Fuzzy c-means
Reliability of the clustering result
Tolerance

ASJC Scopus subject areas

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

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10.1093/ietfec/e90-a.10.2194

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AU - Yasunori, Endo

AU - Sadaaki, Miyamoto

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N2 - 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.

AB - 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.

KW - Clustering

KW - Fuzzy c-means

KW - Reliability of the clustering result

KW - Tolerance

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

Abstract

Keywords

ASJC Scopus subject areas

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