一种新的模糊聚类有效性函数

2009 International Conference on Computational Intelligence and Natural Computing Pub Date : 2009-06-06 DOI:10.1109/CINC.2009.100

Yang Li, Fusheng Yu

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引用次数: 30

摘要

本文首先给出了一种新的模糊聚类有效性函数，然后给出了标准模糊c均值聚类算法中聚类数的最优选择方法，最后概述了参数自适应模糊c均值聚类算法。在合成数据集和基于实际背景的数据集上进行的实验结果说明了新的有效性函数和相应的模糊聚类算法的性能。

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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A New Validity Function for Fuzzy Clustering

This paper first gives a new validity function for fuzzy clustering, then presents a method of the optimal selecting of the cluster number in the standard fuzzy c-means clustering algorithm, and finally outlines the fuzzy c-means clustering algorithm with parameters self-adapted. Experimental results carried on synthetic data set and data set based on actual background illustrate the performance of the new validity function and the corresponding fuzzy clustering algorithm.

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2009 International Conference on Computational Intelligence and Natural Computing

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