关键词: 海量数据, 聚类, MapReduce, K-means算法, Canopy算法

Abstract: In order to solve the problem of the clustering on massive data under the framework of a centralized system, an optimized algorithm to K-means clustering based on MapReduce is proposed. By using MapReduce parallel programming framework and importing Canopy clustering, this algorithm optimizes initial clustering center, improves communication mode and calculation mode in iteration. The experimental results show that this algorithm can effectively improve the quality of clustering, and can have higher implementation efficiency, its good scalability, thus it fits to clustering analysis on massive data.

Key words: massive data, clustering, MapReduce, K-means algorithm, Canopy algorithm