关键词: 海量数据,K-Medoids,MapReduce,遗传算法,聚类效率

Abstract: Huge volumes of data are increasing exponentially with the rapid development of Internet,which poses signifi-cant challenges to traditional clustering technologies.Thus,improving the accuracy and computing performance of clustering has become a research hotspot.As one of the partition-based clustering algorithms,K-Medoids can effectively deal with the problems with isolate and noise points.However,it also suffers from problems such as sensitive to initial centers,easily falling into local optimum,CPU and memory bottlenecks with big data sets.We proposed a genetic algorithm based K-Medoids clustering under MapReduce framework.The algorithm solves the center sensitivity problem of the K-Medoids by using the genetic algorithm.Also,it is built on the MapReduce framework to boost the efficiency both for K-Medoids and the genetic algorithm.The experiments demonstrate that the proposed algorithm can effectively improve the quality and efficiency of clustering.

Key words: Big-data,K-medoids,MapReduce,Genetic algorithms,Clustering efficiency