Every picture tells a story: Visual Clustering in Relational Data06/07/2010
Speaker(s) : James C. Bezdek (Milton, FL, USA)
This 90 minute talk begins with definitions and examples of the three canonical problems of cluster analysis: tendency assessment, clustering, and cluster validity. The second part of this talk is a short history of visual approaches for these three problems. Part three defines and exemplifies the VAT (visual assessment of tendency) and improved iVAT approaches for building a reordered dissimilarity image from relational data that can be used to estimate cluster number and tendency. Part four describes an application of this algorithm to clustering ellipsoids in the context of wireless sensor networks. If time permits I will briefly discuss the sVAT algorithm (VAT for arbitrarily large square data), and coVAT (VAT for rectangular dissimilarity data.
Biographical Information: James C. Bezdek Jim received the PhD in Applied Mathematics from Cornell University in 1973. Jim is past president of NAFIPS (North American Fuzzy Information Processing Society), IFSA (International Fuzzy Systems Association) and the IEEE CIS (Computational Intelligence Society): founding editor the Int'l. Jo. Approximate Reasoning and the IEEE Transactions on Fuzzy Systems: Life fellow of the IEEE and IFSA; and a recipient of the IEEE 3rd Millennium, IEEE CIS Fuzzy Systems Pioneer, and IEEE CIS technical field award Rosenblatt medals. Jim's interests: woodworking, optimization, motorcycles, pattern recognition, cigars, clustering in very large data, fishing, co-clustering, blues music, and visual clustering. Jim retired in 2007, and will be coming to a university near you soon.
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Thomas.Baerecke (at) nulllip6.fr