Clustering aggregation for improving ant based clustering

Authors:
Akil Elkamel;Mariem Gzara;Hanêne Ben-Abdallah
Affiliations:
MIRACL, Sfax, Tunisia and Institut Supérieur d'Informatique et de Mathématiques de Monastir, Monastir, Tunisia;MIRACL, Sfax, Tunisia and Institut Supérieur d'Informatique et de Mathématiques de Monastir, Monastir, Tunisia;MIRACL, Sfax, Tunisia and Faculté des Sciences Économiques et de Gestion de Sfax, Sfax, Tunisia
Venue:
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Year:
2011

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Abstract

In this paper, we propose a hybridization between an antbased clustering algorithm: CAC (Communicating Ants for Clustering) algorithm [5] and a clustering aggregation algorithm: the Furthest algorithm [6]. The CAC algorithm takes inspiration from the sound communication properties of real ants. In this algorithm, artificial ants communicate directly with each other in order to achieve the clustering task. The Furthest algorithm takes as inputsm clusterings given bym different runs of the CAC algorithm, and tries to find a clustering that matches, as possible, all the clusterings given as inputs. This hybridization shows an improvement of the obtained results.