An efficient approximation to the K-means clustering for massive data

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• 作者：Marco Capó ; ; ^a ; ^{mcapo@bcamath.org} ; Aritz Pé ; rez^a ; ^{aperez@bcamath.org} ; Jose A. Lozano^a ; ^b ; ^{ja.lozano@ehu.eus}
• 关键词：K-means ; Clustering ; K-means++ ; Minibatch K-means
• 刊名：Knowledge-Based Systems
• 出版年：2017
• 出版时间：1 February 2017
• 年：2017
• 卷：117
• 期：Complete
• 页码：56-69
• 全文大小：2045 K
• 卷排序：117

文摘

An approximation to the Kmeans algorithm for massive data problems is proposed. RPKM reduces several orders of computations while obtaining good approximations. RPKM reduces the maximum number of Lloyd’s iterations up to a stirling number order. Experimentally, a monotone descent of the error function is consistently observed.