Convergence of alternative C-means clustering algorithms

Kiichi Urahama

Convergence of alternative C-means clustering algorithms

Kiichi Urahama

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

Abstract

The alternative c-means algorithm has recently been presented by Wu and Yang [1] for robust clustering of data. In this letter, we analyze the convergence of this algorithm by transforming it into an equivalent form with the Legendre transform. It is shown that this algorithm converges to a local optimal solution from any starting point.

Original language	English
Pages (from-to)	752-754
Number of pages	3
Journal	IEICE Transactions on Information and Systems
Volume	E86-D
Issue number	4
Publication status	Published - Apr 2003

All Science Journal Classification (ASJC) codes

Information Systems
Computer Graphics and Computer-Aided Design
Software

Cite this

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title = "Convergence of alternative C-means clustering algorithms",

abstract = "The alternative c-means algorithm has recently been presented by Wu and Yang [1] for robust clustering of data. In this letter, we analyze the convergence of this algorithm by transforming it into an equivalent form with the Legendre transform. It is shown that this algorithm converges to a local optimal solution from any starting point.",

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AU - Urahama, Kiichi

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AB - The alternative c-means algorithm has recently been presented by Wu and Yang [1] for robust clustering of data. In this letter, we analyze the convergence of this algorithm by transforming it into an equivalent form with the Legendre transform. It is shown that this algorithm converges to a local optimal solution from any starting point.

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M3 - Letter

AN - SCOPUS:0142217398

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JO - IEICE Transactions on Information and Systems

JF - IEICE Transactions on Information and Systems

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Convergence of alternative C-means clustering algorithms

Abstract

All Science Journal Classification (ASJC) codes

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