A Novel Clustering Method with Ants

摘要：

A novel clustering algorithm named ACO (Ant colony Optimization) based K-means (ACOK) algorithm was proposed which based on heuristic concept. As k-means is easy to obtain local optimum, ACOK use ant colony optimization algorithm to obtain global search. It improves the k-means by locating the objects in a cluster with the probability, which is updated by the pheromone, while the rule of updating pheromone is according to total within cluster variance (TWCV). The experiment results showed that it is better than another famous heuristic based k-means method, genetic algorithm (GA) based k-means algorithm (GKA) and k-means.

关键词： data mining clustering ACO

作者: Yong Wang Wei Zhang

作者单位: the Computer Department of Chongqing education college, Chongqing, 400067, China the Computer Department of Chongqing education college, Chongqing, 400067, China.

会议类型: 国际会议

会议名称: The Second International Symposium on Intelligence Computation and Applications(ISICA 2007)(第二届智能计算及其应用国际会议)

会议地点: 武汉

会议语种:英文

在线出版日期: 2007-09-21（万方平台首次上网日期，不代表论文的发表时间）