Fuzzy Rules for Ant Based Clustering Algorithm
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 observ...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Advances in Fuzzy Systems |
| Online Access: | http://dx.doi.org/10.1155/2016/8198915 |
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| _version_ | 1832564186992869376 |
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| author | Amira Hamdi Nicolas Monmarché Mohamed Slimane Adel M. Alimi |
| author_facet | Amira Hamdi Nicolas Monmarché Mohamed Slimane Adel M. Alimi |
| author_sort | Amira Hamdi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-49254cbbe5fa48309b74a6027f6f2a08 |
| institution | Kabale University |
| issn | 1687-7101 1687-711X |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Fuzzy Systems |
| spelling | doaj-art-49254cbbe5fa48309b74a6027f6f2a082025-02-03T01:11:34ZengWileyAdvances in Fuzzy Systems1687-71011687-711X2016-01-01201610.1155/2016/81989158198915Fuzzy Rules for Ant Based Clustering AlgorithmAmira Hamdi0Nicolas Monmarché1Mohamed Slimane2Adel M. Alimi3REGIM-Lab.: Research Groups in Intelligent Machines, University of Sfax, ENIS, BP 1173, 3038 Sfax, TunisiaPolytech Tours, University of Tours, Tours, FrancePolytech Tours, University of Tours, Tours, FranceREGIM-Lab.: Research Groups in Intelligent Machines, University of Sfax, ENIS, BP 1173, 3038 Sfax, TunisiaThis 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.http://dx.doi.org/10.1155/2016/8198915 |
| spellingShingle | Amira Hamdi Nicolas Monmarché Mohamed Slimane Adel M. Alimi Fuzzy Rules for Ant Based Clustering Algorithm Advances in Fuzzy Systems |
| title | Fuzzy Rules for Ant Based Clustering Algorithm |
| title_full | Fuzzy Rules for Ant Based Clustering Algorithm |
| title_fullStr | Fuzzy Rules for Ant Based Clustering Algorithm |
| title_full_unstemmed | Fuzzy Rules for Ant Based Clustering Algorithm |
| title_short | Fuzzy Rules for Ant Based Clustering Algorithm |
| title_sort | fuzzy rules for ant based clustering algorithm |
| url | http://dx.doi.org/10.1155/2016/8198915 |
| work_keys_str_mv | AT amirahamdi fuzzyrulesforantbasedclusteringalgorithm AT nicolasmonmarche fuzzyrulesforantbasedclusteringalgorithm AT mohamedslimane fuzzyrulesforantbasedclusteringalgorithm AT adelmalimi fuzzyrulesforantbasedclusteringalgorithm |