Logic-oriented fuzzy clustering

Authors:
Witold Pedrycz;George Vukovich
Affiliations:
Department of Electrical and Computer Engineering University of Alberta, 238 Civil/Electrical Engineering Building, Edmonton, Canada T6G 2G7 and Systems Research Institute, Polish Academy of Scien ...;Canadian Space Agency, Spacecraft Engineering, 6767 Route de l'Aeroport, Saint-Hubert, Que., Canada J3Y 8Y9
Venue:
Pattern Recognition Letters
Year:
2002

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Abstract

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.