Fuzzy Rules for Ant Based Clustering Algorithm

Amira Hamdi,Mohamed Slimane,Nicolas Monmarché,Adel M Alimi

doi:10.1155/2016/8198915

Amira Hamdi, Mohamed Slimane + Show 2 more

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https://doi.org/10.1155/2016/8198915

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Abstract

This paper provides a new intelligent technique for semisupervised data clustering problem that combines the Ant System (AS) algorithm with the fuzzyc-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 fuzzyc-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 ofK-means,K-medoid, and FCM algorithms. Experimental section shows that F-ASClass performs better according to the error rate classification, accuracy, and separation index.

Highlights

IntroductionCollective behaviors in swarms of insects or animals have attached the attention of researches
How do ants optimize food search? How do social spiders build communal nest? Why does a flock of birds fly in a vshaped formation? How do termites build collectively their sophisticated nest structure? How do honey bee swarms cooperatively select their new nesting site? How does firefly flash its light in a wonderful pattern? How does a colony coordinate its behavior? How is it possible for social insects and animals to coordinate their actions and create complex patterns? How do such agents perform complex tasks without any direction and coordination between themselves? How agents in colony perform a work locally for global goal with sufficient flexibility as they are not controlled centrally? Collective behaviors in swarms of insects or animals have attached the attention of researches
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

Summary

Introduction

Collective behaviors in swarms of insects or animals have attached the attention of researches. They have proposed several intelligent models to solve a wide range of complex problems. The rules specifying interactions among the system’s components are executed using only local information, without reference to the global pattern” [1]. In short it can be “a set of dynamical mechanisms whereby structures appear at the global level of a system from interactions of its lower-level components” [2]

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