期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2017
卷号：13
期号：8
页码：1
DOI：10.1177/1550147717728627
出版社：Hindawi Publishing Corporation
摘要：K -means plays an important role in different fields of data mining. However, k -means often becomes sensitive due to its random seeds selecting. Motivated by this, this article proposes an optimized k -means clustering method, named k* -means, along with three optimization principles. First, we propose a hierarchical optimization principle initialized by k * seeds ( k * > k ) to reduce the risk of random seeds selecting, and then use the proposed “top- n nearest clusters merging” to merge the nearest clusters in each round until the number of clusters reaches at k . Second, we propose an “optimized update principle” that leverages moved points updating incrementally instead of recalculating mean and SSE of cluster in k -means iteration to minimize computation cost. Third, we propose a strategy named “cluster pruning strategy” to improve efficiency of k -means. This strategy omits the farther clusters to shrink the adjustable space in each iteration. Experiments performed on real UCI and synthetic datasets verify the efficiency and effectiveness of our proposed algorithm.
