Visualizing high density clusters in multidimensional data using optimized star coordinates

Abstract

Multidimensional multivariate data have been studied in different areas for quite some time. Commonly, the analysis goal is not to look into individual records but to understand the distribution of the records at large and to find clusters of records that exhibit correlations between dimensions or variables. We propose a visualization method that operates on density rather than individual records. To not restrict our search for clusters, we compute density in the given multidimensional space. Clusters are formed by areas of high density. We present an approach that automatically computes a hierarchical tree of high density clusters. For visualization purposes, we propose a method to project the multidimensional clusters to a 2D or 3D layout. The projection method uses an optimized star coordinates layout. The optimization procedure minimizes the overlap of projected clusters and maximally maintains the cluster shapes, compactness, and distribution. The star coordinate visualization allows for an interactive analysis of the distribution of clusters and comprehension of the relations between clusters and the original dimensions. Clusters are being visualized using nested sequences of density level sets leading to a quantitative understanding of information content, patterns, and relationships.