文章基本信息

标题：PARALLEL K-MEANS FOR BIG DATA: ON ENHANCING ITS CLUSTER METRICS AND PATTERNS
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作者：VERONICA S. MOERTINI ; LIPTIA VENICA
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2017
卷号：95
期号：8
出版社：Journal of Theoretical and Applied
摘要：K-Means clustering algorithm has been enhanced based on MapReduce such that it works in distributed Hadoop cluster for clustering big data. We found that the existing algorithm have not included techniques for computing the cluster metrics necessary for evaluating the quality of clusters and finding interesting patterns. This research adds this capability. Few metrics are computed in every iteration of k-Means in the Hadoop�s Reduce function such that when it is converged, the metrics are ready to be evaluated. We have implemented the proposed parallel k-Means and the experiments results show that the proposed metrics are useful for selecting clusters and finding interesting patterns.
关键词：Clustering Big Data; Parallel k-Means; Hadoop MapReduce