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Comparing soft clusters and partitions

机译:比较软集群和分区

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摘要

Previously, we presented a method for comparing soft partitions (i.e. crisp, probabilistic, fuzzy and possibilistic) to a known crisp reference partition. Many of the classical indices that have been used with outputs of crisp clustering algorithms were generalized so that they are applicable for candidate partitions of any type. In particular, focus was placed on generalizations of the Rand index. In this article, we extend our prior work by (1) investigating the behavior of the soft Rand for comparing non-crisp, specifically possibilistic, partitions and (2) we demonstrate how the possibilistic Rand and visual assessment of (cluster) tendency (VAT) algorithm can be used to discover the number of actual clusters and coincident clusters for outputs from the possibilistic c-means (PCM) algorithm.
机译:以前,我们提出了一种将软分区(即明快,概率,模糊和可能)与已知明快参考分区进行比较的方法。归纳了许多与清晰聚类算法的输出一起使用的经典索引,因此它们适用于任何类型的候选分区。特别地,重点放在兰德指数的概括上。在本文中,我们将通过(1)研​​究软兰德的行为来比较非酥脆(特别是可能性)分区来扩展我们的先前工作,并且(2)我们演示如何对兰特进行可能性和对(集群)趋势(VAT)的视觉评估)算法可用于发现实际c均值(PCM)算法输出的实际簇和重合簇的数量。

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