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On fuzzy cluster validity indices

机译:关于模糊聚类有效性指标

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Cluster analysis aims at identifying groups of similar objects, and helps to discover distribution of patterns and interesting correlations in large data sets. Especially, fuzzy clustering has been widely studied and applied in a variety of key areas and fuzzy cluster validation plays a very important role in fuzzy clustering. This paper introduces the fundamental concepts of cluster validity, and presents a review of fuzzy cluster validity indices available in the literature. We conducted extensive comparisons of the mentioned indices in conjunction with the Fuzzy C-Means clustering algorithm on a number of widely used data sets, and make a simple analysis of the experimental results.
机译:聚类分析旨在识别相似对象的组,并有助于发现大型数据集中模式的分布和有趣的相关性。特别地,模糊聚类已被广泛研究并应用于各种关键领域,并且模糊聚类验证在模糊聚类中起着非常重要的作用。本文介绍了聚类有效性的基本概念,并对文献中可用的模糊聚类有效性指标进行了综述。我们结合模糊C均值聚类算法对许多广泛使用的数据集进行了上述指标的广泛比较,并对实验结果进行了简单分析。

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