Tendency curves for visual clustering assessment

Authors:
Yingkang Hu;Richard J. Hathaway
Affiliations:
Georgia Southern University, Department of Mathematical Sciences, Statesboro, GA;Georgia Southern University, Department of Mathematical Sciences, Statesboro, GA
Venue:
ACC'08 Proceedings of the WSEAS International Conference on Applied Computing Conference
Year:
2008

Citing 5
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Algorithms for clustering data

Algorithms for clustering data
Visualizing Data

Visualizing Data
Scalable visual assessment of cluster tendency for large data sets

Pattern Recognition
bigVAT: Visual assessment of cluster tendency for large data sets

Pattern Recognition
Visual Assessment of Clustering Tendency for Rectangular Dissimilarity Matrices

IEEE Transactions on Fuzzy Systems

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Abstract

We improve the visual assessment of tendency (VAT) technique, which, developed by J.C. Bezdek, R.J. Hathaway and J.M. Huband, uses a visual approach to find the number of clusters in data. Instead of using square gray level images of dissimilarity matrices as in VAT, we further process the matrices and produce the tendency curves. Possible cluster structure will be shown as peak-valley patterns on the curves, which can be caught not only by human eyes but also by the computer. Our numerical experiments showed that the computer can catch cluster structures from the tendency curves even in cases where the visual outputs of VAT are virtually useless.