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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Published in | Image Analysis pp. 511 - 520 |
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Main Authors | , |
Format | Book Chapter Conference Proceeding |
Language | English |
Published |
Berlin, Heidelberg
Springer Berlin Heidelberg
2005
Springer |
Series | Lecture Notes in Computer Science |
Subjects | |
Online Access | Get full text |
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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. |
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ISBN: | 9783540263203 3540263209 |
ISSN: | 0302-9743 1611-3349 |
DOI: | 10.1007/11499145_52 |