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Keynote Lecture - Prof. Alfred Inselberg

Last modified 2014-07-04 17:48

Visualization and Data Mining for High Dimensional Datasets
Prof. Alfred Inselberg
School of Mathematical Sciences, Tel Aviv University, Israel
Senior Fellow, San Diego Supercomputing Center
Inventor of the multidimensional system of Parallel Coordinates and author of textbook "Parallel Coordinates: VISUAL Multidimensional Geometry", (praised by Stephen Hawking among others.)

Date & Time: 09:45am - 10:45am; July 21 (Monday), 2014
LOCATION: Monte Carlo (Blue Man Group) Theater


    A dataset with M items has 2M subsets anyone of which may be the one satisfying our objectives. With a good data display and interactivity our fantastic pattern-recognition can not only cut great swaths searching through this combinatorial explosion, but also extract insights from the visual patterns. These are the core reasons for data visualization. With parallel coordinates the search for relations in multivariate datasets is transformed into a 2-D pattern recognition problem. Guidelines and strategies for knowledge discovery are illustrated on several real datasets (financial, process control, credit-score and one with hundreds of variables) with stunning results. A geometric classification algorithm, having low computational complexity, provides the classification rule explicitly and visually. The minimal set of variables, features, required to state the rules is found and ordered by their predictive value. Multivariate relations can be modeled as hypersurfaces and used for decision support. A model of a (real) country’s economy reveals sensitivities, impact of constraints, trade-offs and economic sectors unknowingly competing for the same resources. A smart display for Intensive Care Units determines the patient’s state by the interaction of many variables. An overview of the methodology provides foundational understanding; learning the patterns corresponding to various multivariate relations. These patterns are robust in the presence of errors and that is good news for the applications. A topology of proximity emerges opening the way for visualization in Big Data. A 4-D animated visualization for Special Relativity explains Time Dilation and other “strange” phenomena.


    Al received a Ph.D. in Mathematics and Physics from the University of Illinois (UICU). He was graduate assistant at the Biological Computer Lab (BCL), where research on Brain Function, Cognition and Learning was carried out (coupled to McCulloch’s Lab at MIT), and continued as Research Asst. Prof. From 1966-1995 he was IBM researcher (reaching a rank just below Fellow) at the Los Angeles Scientific Center and later Yorktown Labs. He developed a Mathematical Model of the (Inner) Ear (TIME, Newsweek 1974) concurrently teaching at UCLA and USC. He joined the Technion’s faculty 1971-73, Ben Gurion University 1977-83, and is at Tel Aviv University since 1995. AI was elected Senior Fellow in Visualization at the San Diego Supercomputing Center (1996), Distinguished Visiting Professor at the Korea University (2008) and National University of Singapore (2011). He invented the multidimensional visualization methodology of Parallel Coordinates which has become widely accepted and applied (Air Traffic Control, Data Mining etc). His textbook on the subject, published by Springer, was praised by Stephen Hawking among others.
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