A dataset with M items has 2M subsets anyone of which may be the one satisfying our objective. With a good data display and interactivity our fantastic pattern-recognition defeats this combinatorial explosion by extracting insights from the visual patterns. This is the core reason for data visualization. With parallel coordinates the search for relations in multivariate data is transformed into a 2-D pattern recognition problem. Together with criteria for good query design, we illustrate this on several real datasets (financial, process control, credit-score, one with hundreds of variables) with stunning results. A geometric classification algorithm yields the classification rule explicitly and visually. The minimal set of variables, features, are found and ordered by their predictive value. A model of a country’s economy reveals sensitivities, impact of constraints, trade-offs and economic sectors unknowingly competing for the same resources. An overview of the methodology provides foundational understanding; learning the patterns corresponding to various multivariate relations (linearity, convexity, even non-orientability i.e. Moebius and more) 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.


Alfred Inselberg Alfred Inselberg received a Ph. D. in Mathematics and Physics from the University of Illinois (Champaign-Urbana) and continued as Research Professor at the Biological Computer Laboratory there working on Neural Networks, Cognition, Population Dynamics and Models of Complex Nonlinear Systems. He then held senior research positions at IBM where he developed a Mathematical Model of the Ear (Cochlea) (TIME Nov. 74) and later Collision-Avoidance Algorithms for Air Traffic Control (3 USA patents). Concurrently he had joint appointments at UCLA, USC, Technion and Ben Gurion University. Since 1995 AI is Professor at the School of Mathematical Sciences of Tel Aviv University. He was elected Senior Fellow at the San Diego Supercomputing Center in 1996, Distinguished Visiting Professor at Korea University in 2008, and National University of Singapore in 2011. He invented and developed the multidimensional visualization methodology of Parallel Coordinates for which he received numerous patents and awards. His textbook on “Parallel Coordinates: VISUAL Multidimensional Geometry”, was published by Springer and praised by Stephen Hawking among many others. — Source: https://cetic.cm/teacher/alfred-inselberg-ai/