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A New Cluster Validity Index for Fuzzy Clustering Based on Similarity Measure

机译:基于相似度度量的模糊聚类新有效性指标

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In this paper, first, the main problems of some cluster validity indices when they have been applied to Gustafson and Kessel (GK) clustering approach are review. It is shown that most of these cluster validity indices have serious shortcomings to validate Gustafson Kessel algorithm. Then, a new cluster validity index based on a similarity measure of fuzzy clusters for validation of GK algorithm is presented. This new index is not based on a geometric distance and can determine the degree of correlation of the clusters. Finally, the proposed cluster validity index is tested and validated by using five sets of artificially generated data. The results show that the proposed cluster validity index is more efficient and realistic than the former traditional indices.
机译:本文首先回顾了将某些聚类有效性指标应用于Gustafson和Kessel(GK)聚类方法时的主要问题。结果表明,大多数聚类有效性指标在验证Gustafson Kessel算法方面都存在严重缺陷。然后,提出了一种基于模糊聚类相似性度量的聚类有效性指标,用于GK算法的验证。该新索引不是基于几何距离,而是可以确定聚类的相关程度。最后,使用五组人工生成的数据对提出的聚类有效性指标进行了测试和验证。结果表明,所提出的聚类有效性指标比传统指标更为有效和现实。

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