VCV2: visual cluster validity

Authors:
Jacalyn M. Huband;James C. Bezdek
Affiliations:
Computer Science Department, University of West Florida, Pensacola, FL;Department of Computer Science and Software Engineering, University of Melbourne, Melbourne, Victoria, Australia
Venue:
WCCI'08 Proceedings of the 2008 IEEE world conference on Computational intelligence: research frontiers
Year:
2008

Citing 4
Cited 1

Scalable visual assessment of cluster tendency for large data sets

Pattern Recognition
Some new indexes of cluster validity

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Visual Assessment of Clustering Tendency for Rectangular Dissimilarity Matrices

IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model

IEEE Transactions on Fuzzy Systems

Is VAT really single linkage in disguise?

Annals of Mathematics and Artificial Intelligence

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Abstract

All clustering algorithms partition data into a specified or algorithmically determined number of clusters, whether or not that number of clusters actually exists in the data. Therefore, identifying a "best" solution amongst a set of candidate partitions is an important step in the clustering process. This paper presents a visual technique for comparing found partitions with a pre-clustering VAT (Visual Assessment of cluster Tendency) image of the unlabeled input data. The method is developed independent of any particular clustering algorithm, and then illustrated with numerical examples that use the fuzzy c-means clustering method. The experiments use samples from mixtures of bivariate normals, a bivariate uniform, and a small real data set to illustrate the efficacy of the method.