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Communication Dans Un Congrès ICTAI '12 : The IEEE 24th International Conference on Tools with Artificial Intelligence Année : 2012

A new algorithm for fuzzy clustering able to find the optimal number of clusters

Résumé

Tackling, within a classification task, to the problem of inaccuracy explains the development of new theories that offer a formal treatment of imprecise information, especially the theory of fuzzy sets who suggested a new approach taking advantage of the concept of membership function. Nevertheless, clustering algorithms still show limits, particularly for the estimation of the number of clusters. In this paper, through a state of the art of the main fuzzy classification algorithms, we introduce a new algorithm, called Fuzzy-MSOM. The latter aims at palliating to drawback of the determination of the suitable number of clusters in a given data set. Thus, the clustering process is carried out through a multi-level approach. Through the use of fuzzy clustering validity indices, Fuzzy-MSOM overcomes the problem of the estimation of clusters number. The experimental result shows that the proposed clustering technique provides better results compared to the previous algorithms.
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Dates et versions

hal-00831437 , version 1 (07-06-2013)

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Balkis Abidi, Sadok Ben Yahia, Amel Bouzeghoub. A new algorithm for fuzzy clustering able to find the optimal number of clusters. ICTAI '12 : The IEEE 24th International Conference on Tools with Artificial Intelligence, Nov 2012, Athens, Greece. pp.806-813, ⟨10.1109/ICTAI.2012.174⟩. ⟨hal-00831437⟩
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