On Probabilistic k-Richness of the k-Means Algorithms

被引:2
作者
Klopotek, Robert A. [1 ]
Klopotek, Mieczyslaw A. [2 ]
机构
[1] Cardinal Stefan Wyszynski Univ Warsaw, Fac Math & Nat Sci, Sch Exact Sci, Warsaw, Poland
[2] Polish Acad Sci, Comp Sci Fundamental Res Inst, Warsaw, Poland
来源
MACHINE LEARNING, OPTIMIZATION, AND DATA SCIENCE | 2019年 / 11943卷
关键词
k-means; k-means plus; k-richness; Probabilistic k-richness; Weak probabilistic k-richness;
D O I
10.1007/978-3-030-37599-7_22
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
With Kleinberg's axiomatic system for clustering, a discussion has been initiated, what kind of properties clustering algorithms should have and have not. As Ackerman et al. pointed out, the static properties studied by Kleinberg and other are not appropriate for clustering algorithms with elements of randomness. Therefore they introduced the property of probabilistic k-richness and claimed, without a proof that the versions of k-means both with random initialisation and k-means++ initialization have this property. We prove that k-means++ has the property of probabilistic k-richness, while k-means with random initialisation for well separated clusters does not. To characterize the latter, we introduce the notion of weak probabilistic k-richness and prove it for this algorithm. For completeness, we provide with a constructive proof that the theoretical k-means has the (deterministic) k-richness property.
引用
收藏
页码:259 / 271
页数:13
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