 An effective and efficient hierarchical K-means clustering algorithmJianpeng Qi, Yanwei Yu, Lihong Wang, Jinglei Liu and Yingjie Wang International Journal of Distributed Sensor Networks, 2017, vol. 13, issue 8, 1550147717728627 Abstract: 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. Keywords: Data mining; clustering; k-means; top-n merging; cluster pruning (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:13:y:2017:i:8:p:1550147717728627 Access Statistics for this article More articles in International Journal of Distributed Sensor Networks |
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