M-Estimator induced Fuzzy Clustering Algorithms

R. Winkler, F. Klawonn, R. Kruse
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引用次数: 1

Abstract

M-estimators can be seen as a special case of robust clustering algorithms. In this paper, we present the reversed direction and show that clustering algorithms can be constructed by using M-estimators. A clever normalization is used to link the values of several M-estimator prototypes together in one clustering algorithm. A variety of M-estimators and several normalization strategies are used in 4 data sets to present their dierences and properties. The results are evaluated using 5 dierent clustering validation indices.
m估计诱导模糊聚类算法
m估计器可以看作是鲁棒聚类算法的一个特例。在本文中,我们提出了相反的方向,并证明了聚类算法可以用m估计量构造。在一个聚类算法中,使用一种巧妙的归一化方法将多个m估计量原型的值连接在一起。在4个数据集中使用了各种m估计量和几种归一化策略来展示它们的区别和性质。使用5种不同的聚类验证指标对结果进行评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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