Hybridization of the ant colony optimization with the k-means algorithm for clustering

Authors:
Sara Saatchi;Chih Cheng Hung
Affiliations:
Department of Computer Science, Southern Polytechnic State University, Marietta, GA;Department of Computer Science, Southern Polytechnic State University, Marietta, GA
Venue:
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Year:
2005

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Abstract

In this paper the novel concept of ACO and its learning mechanism is integrated with the K-means algorithm to solve image clustering problems. The learning mechanism of the proposed algorithm is obtained by using the defined parameter called pheromone, by which undesired solutions of the K-means algorithm is omitted. The proposed method improves the K-means algorithm by making it less dependent on the initial parameters such as randomly chosen initial cluster centers, hence more stable.