A cluster validity index based on frequent pattern
Hongyan Cui, Kuo Zhang, Xu Huang, Yunjie Liu · University of Canberra Research Portal · 2013
Since a clustering algorithm can produce as many partitions as desired, one need to assess their quality in order to select the partition that most represents the structure in the data. This is the rationale for the cluster-validity (CV) problem and indices. This paper proposes a CV index for fuzzy-clustering algorithm, such as the fuzzy c-means (FCM) or its derivatives. Given a fuzzy partition, this new index uses global information and is based on more logical reasoning than geometrical features. Experimental results on artificial and benchmark datasets are given to demonstrate the performance of the proposed index, as compared with traditional and recent indices.