Hybridization of the Ant Colony Optimization with the K-Means Algorithm for Clustering

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 alg...

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Bibliographic Details
Published inImage Analysis pp. 511 - 520
Main Authors Saatchi, Sara, Hung, Chih Cheng
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2005
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Exact sciences and technology
Pattern recognition. Digital image processing. Computational geometry
Cluster analysis
K means algorithm
Swarm intelligence
Hybridization
Image processing
Classification
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Summary: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.
ISBN:9783540263203
3540263209
ISSN:0302-9743
1611-3349
DOI:10.1007/11499145_52