Visualizing multi-dimensional clusters, trends, and outliers using star coordinates

Abstract

Interactive visualizations are effective tools in mining scientific, engineering, and business data to support decision-making activities. Star Coordinates is proposed as a new multi-dimensional visualization technique, which supports various interactions to stimulate visual thinking in early stages of knowledge discovery process. In Star Coordinates, coordinate axes are arranged on a two-dimensional surface, where each axis shares the same origin point. Each multi-dimensional data element is represented by a point, where each attribute of the data contributes to its location through uniform encoding. Interaction features of Star Coordinates provide users the ability to apply various transformations dynamically, integrate and separate dimensions, analyze correlations of multiple dimensions, view clusters, trends, and outliers in the distribution of data, and query points based on data ranges. Our experience with Star Coordinates shows that it is particularly useful for the discovery of hierarchical clusters, and analysis of multiple factors providing insight in various real datasets including telecommunications churn.