A VISUALIZATION FRAMEWORK FOR THE ANALYSIS OF HYPERDIMENSIONAL DATA

The purpose of this article is to describe a new visualization framework for the analysis of hyperdimensional data. This framework was developed in order to facilitate the study of a new class of classifiers designated class cover catch digraphs. The class cover catch digraph is an original random graph technique for the construction of classifiers on high dimensional data. This framework allows the user to study the geometric structure of hyperdimensional data sets via the reduction of the original hyperdimensional space to a cover with a small number of balls. The framework allows for the elicitation of geometric and other structures through the visualization of the relationships between the balls and each other and the observations they cover. Keywords: Graph; Classifier; Visualization; Data Mining. 1