Displaying data in multidimensional relevance space with 2D visualization maps

This paper introduces a tool for visualizing a multidimensional relevance space. Abstractly, the information to be displayed consists of a large number of objects, a set of features that are likely to be of interest to the user, and some function that measures the relevance level of every object to the various features. The goal is to provide the user with a concise and comprehensible visualization of that information. For the type of applications we concentrate on, the exact relevance measures of the objects are not significant. This enables accuracy to be traded for a clearer display. The idea is to 'flatten' the multidimensionality of the feature space into a 2D 'relevance map', capturing the inter-relations among the features, without causing too many ambiguous interpretations of the results. To better reflect the nature of the data and to resolve the ambiguity we refine the given set of features and introduce the notion of composed features. The layout of the map is then obtained by grading it according to a set of rules and using a simulated annealing algorithm which optimizes the layout with respect to these rules. The technique we propose here has been implemented and tested, in the context of visualizing the result of a Web search, in the RMAP (Relevance Map) prototype system.