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Fuzzy Rules for Ant Based Clustering Algorithm
Joint Authors
Alimi, Adel M.
Hamdi, Amira
Monmarché, Nicolas
Slimane, Mohamed
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-10-27
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Hamdi, Amira& Monmarché, Nicolas& Slimane, Mohamed& Alimi, Adel M.. 2016. Fuzzy Rules for Ant Based Clustering Algorithm. Advances in Fuzzy Systems،Vol. 2016, no. 2016, pp.1-16.
https://search.emarefa.net/detail/BIM-1095084
Modern Language Association (MLA)
Hamdi, Amira…[et al.]. Fuzzy Rules for Ant Based Clustering Algorithm. Advances in Fuzzy Systems No. 2016 (2016), pp.1-16.
https://search.emarefa.net/detail/BIM-1095084
American Medical Association (AMA)
Hamdi, Amira& Monmarché, Nicolas& Slimane, Mohamed& Alimi, Adel M.. Fuzzy Rules for Ant Based Clustering Algorithm. Advances in Fuzzy Systems. 2016. Vol. 2016, no. 2016, pp.1-16.
https://search.emarefa.net/detail/BIM-1095084
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1095084