IEEE Toronto Section


Every Picture Tells a Story: Visual Cluster Assessment in Square and Rectangular Relational Data

Monday December 7, 2015 at 4:00 p.m. Professor Emeritus James Bezdek will be presenting “Every Picture Tells a Story: Visual Cluster Assessment in Square and Rectangular Relational Data”.

Speaker: Emeritus James Bezdek
Past President of NAFIPS, IFSA and the IEEE CIS

Day & Time: Monday, December 7, 2015
4:00 p.m. – 6:00 p.m.

Location: Room 1180
Bahen Center for Information Technology
40 St. George Street, Toronto

Organizer: IEEE Toronto Signals & Computational Intelligence Chapter
Distinguished Lecturer Program

Contact: Lorenzo Livi,

Abstract: The VAT/iVAT, algorithms are the parents of a large family of visual assessment models.

Part 1. Definitions of the three canonical problems of cluster analysis: tendency assessment, clustering, and cluster validity. History of Visual Clustering. Applications: role-based compliance assessment, eldercare time series data, and anomaly detection in wireless sensor networks.

Part 2. Extension to siVAT, scalable iVAT for big data. This is the basis of clusiVAT and clusiVAT+ for clustering in big data (Topic 4 below). Application: image segmentation. Extension to coiVAT for assessment of co-clustering tendency in the four clustering problems associated with rectangular relational data. Application: response of 18 Fetal Bovine Serum Treatments to the treatment of fibroblasts in gene expression data.

Biography: 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 technical field award Rosenblatt medals. Jim’s interests: woodworking, optimization, motorcycles, pattern recognition, cigars, clustering in very large data, fishing, co-clustering, blues music, wireless sensor networks, poker and visual clustering. And of course, clustering in big data. Jim retired in 2007, and will be coming to a university near you soon.

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