A Cluster Validity Framework Based on Induced Partition Dissimilarity

作者：Popescu Mihail^*; Bezdek James C; Havens Timothy C; Keller James M

来源：IEEE Transactions on Cybernetics, 2013, 43(1): 308-320.

DOI：10.1109/TSMCB.2012.2205679

• Cluster validity
• induced partition dissimilarity
• matrix correlation
• visual cluster validity

We describe a new cluster validity framework (CVF) that compares structure in the data (in dissimilarity form) to the structure of dissimilarity matrices induced by a matrix transformation of the partition being tested. As part of this framework, we show two possible cluster validation measures: one, visual cluster validity, that that uses visual comparison and another one, correlation cluster validity, based on correlation. Unlike many existing measures, the measures we propose can be applied to crisp or soft partitions obtained by any relational or object data clustering algorithm. We illustrate the new measures and compare them to several well-known existing measures using real and artificial data sets.

• 出版日期2013-2