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Article Dans Une Revue Advances in Fuzzy Systems Année : 2016

Fuzzy Rules for Ant Based Clustering Algorithm

Résumé

This paper provides a new intelligent technique for semisupervised data clustering problem that combines the Ant System (AS) algorithm with the fuzzy c -means (FCM) clustering algorithm. Our proposed approach, called F-ASClass algorithm, is a distributed algorithm inspired by foraging behavior observed in ant colonyT. The ability of ants to find the shortest path forms the basis of our proposed approach. In the first step, several colonies of cooperating entities, called artificial ants, are used to find shortest paths in a complete graph that we called graph-data. The number of colonies used in F-ASClass is equal to the number of clusters in dataset. Hence, the partition matrix of dataset founded by artificial ants is given in the second step, to the fuzzy c -means technique in order to assign unclassified objects generated in the first step. The proposed approach is tested on artificial and real datasets, and its performance is compared with those of K -means, K -medoid, and FCM algorithms. Experimental section shows that F-ASClass performs better according to the error rate classification, accuracy, and separation index.
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Dates et versions

hal-03587095 , version 1 (21-12-2023)

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Amira Hamdi, Nicolas Monmarché, Mohamed Slimane, Adel Alimi. Fuzzy Rules for Ant Based Clustering Algorithm. Advances in Fuzzy Systems, 2016, Lecture Notes in Networks and Systems, 2016, pp.1-16. ⟨10.1155/2016/8198915⟩. ⟨hal-03587095⟩
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