Generation of interpretable fuzzy granules by a double-clustering technique

• Autorzy: Castellano G. , Fanelli A. M. , Mencar C.
• Języki publikacji: EN

Abstrakty

EN

This paper proposes an approach to derive fuzzy granules from numerical data. Granules are first formed by means of a double-clustering technique, and then properly fuzzyfied so as to obtain interpretable granules, in the sense that they can be described by linquistic labels. The double-clustering technique involves two steps. First, information granules are induced in the space of numerical data via the FCM algorithm. In the second step, the prototypes obtained in the first step are further clustered along each dimension via a hierarchical clustering, in order to obtain one-dimensional granules that are afterwards quantified as fuzzy sets. The derived fuzzy sets can be used as building blocks of fuzzy rule-based model. The approach is illustrated with the aid of a benchmark classification example that provides insight into the interpretability of the induced granules and their effect on the results of classification.

Słowa kluczowe

EN

information granulation; fuzzy clustering; fuzzy rules

• Wydawca: Polish Academy of Sciences, Committee of Automatic Control and Robotics
• Czasopismo: Archives of Control Sciences
• Rocznik: 2002
• Tom: Vol. 12, no. 4
• Strony: 397--410
• Opis fizyczny: Bibliogr. 13 poz., rys., tab.

Twórcy

autor

Castellano G.

• Computer Science Department, University of Bari Bari, Italy

autor

Fanelli A. M.

• Computer Science Department, University of Bari Bari, Italy

autor

Mencar C.

• Computer Science Department, University of Bari Bari, Italy

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