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

被引：0
|
作者：
Saatchi, S [1 ]
Hung, CC [1 ]
机构：
[1] So Polytech State Univ, Dept Comp Sci, Marietta, GA 30060 USA
来源：
IMAGE ANALYSIS, PROCEEDINGS | 2005年 / 3540卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：511 / 520
页数：10
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