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论文类型：期刊论文

发表时间：2016-03-01

发表刊物：Journal of Computational and Theoretical Nanoscience

收录刊物：EI、Scopus

卷号：13

期号：3

页面范围：2157-2161

ISSN号：15461955

摘要：A novel cluster validity index is proposed for the validation of partitions for object data. The cluster validity index ranges from the creation of the subclasses produced by the fuzzy c-means algorithm up to their fusion which implies proximity tree according to the largest separation criterion. Testing of the proposed index and four previously formulated indices on well-known data sets shows the superior effectiveness and reliability of the proposed index in comparison with other indices. Copyright ? 2016 American Scientific Publishers. All rights reserved.