Title :
Correlation cluster validity
Author :
Popescu, Mihail ; Keller, James M. ; Bezdek, James C. ; Havens, Timothy
Author_Institution :
HMI, Univ. of Missouri, Columbia, MO, USA
Abstract :
A common question asked about unlabeled data sets is how many subsets (or clusters) of objects are represented in the data? The answer to this question is usually obtained by first clustering the data, and then employing a cluster validity measure to validate one or more candidate partitions of the objects. In this paper we describe an universal cluster validity measure that, unlike most existing measures, can be applied to partitions obtained by any relational or object data clustering algorithm. We illustrate the new measure, and compare it to several well known existing measures using a variety of artificial data sets.
Keywords :
data analysis; matrix algebra; pattern clustering; cluster validity measure; correlation cluster validity; matrix correlation; object data clustering algorithm; object subset; relational data clustering algorithm; unlabeled data set; Algorithm design and analysis; Clustering algorithms; Correlation; Equations; Indexes; Partitioning algorithms; Vectors; cluster validity; matrix correlation; ordered dissimilarity data; relational clustering;
Conference_Titel :
Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
978-1-4577-0652-3
DOI :
10.1109/ICSMC.2011.6084057