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On Probabilistic k-Richness of the k-Means Algorithms

机译:关于K-Means算法的概率k丰制性

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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.
机译:随着Kleinberg的群集的公理系统,已启动讨论,群体群集算法应该具有哪种类型,并且没有。作为Ackerman等。指出,Kleinberg和其他研究所研究的静态属性不适用于随机性元素的聚类算法。因此,他们介绍了概率k-richness和声称的属性,而且没有证明k-mease的版本既具有随机初始化和k均值++初始化都有此属性。我们证明了K-Means ++具有概率k-chiciness的属性,而K-milite具有用于良好分离的群集的随机初始化的k型均不会。为了表征后者,我们介绍了弱概率k丰的概念,并证明了这种算法。为了完整性,我们提供了理论k-means的建设性证据,即(确定性)K-Richness财产。

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