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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Saatchi, S., & Hung, C. C. (2005). Hybridization of the ant colony optimization with the k-means algorithm for clustering. In Lecture Notes in Computer Science (Vol. 3540, pp. 511–520). Springer Verlag. https://doi.org/10.1007/11499145_52
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