An improved ant colony clustering algorithm based on dynamic neighborhood
To solve the problems of the excessive clustering time consumption and the redundant numbers of the resulting clusters, commonly encountered with the ant-based clustering algorithms, an improved ant colony clustering algorithm based on dynamic neighborhood is proposed in this paper. The algorithm seeks for pure neighborhoods by performing auto adaptive adjustments of dynamic neighborhood, and enhances ants memory by additionally storing the sizes of the pure neighborhoods. The ant can exchange information with other ants, load multiple similar objects at once, and merge the similar neighborhoods to form the final clusters efficiently. Experimental results indicate that this algorithm significantly improves the efficiency and quality of ant colony clustering.
ant colony clustering algorithm dynamic neighborhood mult-load
MAO Li SHEN Ming-ming
Key Laboratory of Genetic Breeding and Aquaculture Biology of Kreshwater Fishes, Ministry of Agricul Key Laboratory of Genetic Breeding and Aquaculture Biology of Kreshwater Fishes, Ministry of Agricul
国际会议
厦门
英文
730-734
2010-10-29（万方平台首次上网日期，不代表论文的发表时间）