Logic-oriented fuzzy clustering

Witold Pedrycz

doi:10.1016/S0167-8655(02)00115-0

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Logic-oriented fuzzy clustering

2002-11-01

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Witold Pedrycz *

George Vukovich

DOI：10.1016/S0167-8655(02)00115-0

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Abstract

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The paper is concerned with a logic-based expansion of the standard FCM clustering. The proposed algorithm captures the logic fabric of the structure in a dataset by describing it in the form of a union of the clusters (that is fuzzy relations) determined by the clustering algorithm. In contrast to the standard FCM, the elements (clusters) are combined together as a union of such fuzzy relations-clusters and this form of combination arises as a constraint in the clustering method. In this sense, the introduced clustering environment gives rise to the clustering that is regarded as a logic-driven data decomposition. A detailed algorithm is presented along with some illustrative examples. (C) 2002 Elsevier Science B.V. All rights reserved.

Keywords:

fuzzy clustering

objective function minimization

logic decomposition of data

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