关键词: 数据挖掘, MapReduce, 蚁群优化, K-means, 云计算

Abstract: There is a serious lack of memory when use K-means to deal with massive data. In this paper MapReduce is used to parallelize K-means. Due to ordinary K-means has slow rate of convergence, easily fall into local optimization and the limitations of selection of initial cluster centers, Ant Colony Optimization（ACO） is led into K-means. The result demonstrates ACO-K-means clustering algorithm based on MapReduce model has high speedup and good scalability, and it’s convergence and clustering accuracy are also improved in some degree.

Key words: data mining, MapReduce, Ant Colony Optimization（ACO）, K-means, cloud computing