Regularized fuzzy c-means clustering and its behavior at point of infinity

Yuchi Kanzawa; Sadaaki Miyamoto

doi:10.20965/jaciii.2019.p0485

Regularized fuzzy c-means clustering and its behavior at point of infinity

Yuchi Kanzawa, Sadaaki Miyamoto

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

5 Citations (Scopus)

Abstract

This study shows that a general regularized fuzzy cmeans (rFCM) clustering algorithm, including some conventional clustering algorithms, can be constructed if a given regularizer function value, its derivative function value, and its inverse derivative function value can be calculated. Furthermore, the results of the study show that the behavior of the fuzzy classification function for rFCM at an infinity point is similar to that for some conventional clustering algorithms.

Original language	English
Pages (from-to)	485-492
Number of pages	8
Journal	Journal of Advanced Computational Intelligence and Intelligent Informatics
Volume	23
Issue number	3
DOIs	https://doi.org/10.20965/jaciii.2019.p0485
Publication status	Published - 2019 May

Keywords

Fuzzy clustering
regularization

ASJC Scopus subject areas

Human-Computer Interaction
Computer Vision and Pattern Recognition
Artificial Intelligence

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10.20965/jaciii.2019.p0485

Cite this

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abstract = "This study shows that a general regularized fuzzy cmeans (rFCM) clustering algorithm, including some conventional clustering algorithms, can be constructed if a given regularizer function value, its derivative function value, and its inverse derivative function value can be calculated. Furthermore, the results of the study show that the behavior of the fuzzy classification function for rFCM at an infinity point is similar to that for some conventional clustering algorithms.",

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AB - This study shows that a general regularized fuzzy cmeans (rFCM) clustering algorithm, including some conventional clustering algorithms, can be constructed if a given regularizer function value, its derivative function value, and its inverse derivative function value can be calculated. Furthermore, the results of the study show that the behavior of the fuzzy classification function for rFCM at an infinity point is similar to that for some conventional clustering algorithms.

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Regularized fuzzy c-means clustering and its behavior at point of infinity

Abstract

Keywords

ASJC Scopus subject areas

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