Paper Info
Title
Visual analytics for high dimensional data.
Abstract
A dataset with M items has 2 M 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 countryu0027s 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. 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.
Year
Venue
Field
2018
AVI
Data mining,Data visualization,Clustering high-dimensional data,Classification rule,Computer science,Visualization,Visual analytics,Parallel coordinates,Human–computer interaction,Combinatorial explosion,Big data
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
1
Name
Order
Citations
PageRank
Alfred Inselberg11230165.81