Multidimensional Data Visual Exploration by Interactive Information Segments

Visualization techniques provide an outstanding role in KDD process for data analysis and mining. However, one image does not always convey successfully the inherent information from high dimensionality, very large databases. In this paper we introduce VSIS (Visual Set of Information Segments), an interactive tool to visually explore multidimensional, very large, numerical data. Within the supervised learning, our proposal approaches the problem of classification by searching of meaningful intervals belonging to the most relevant attributes. These intervals are displayed as multi–colored bars in which the degree of impurity with respect to the class membership can be easily perceived. Such bars can be re–explored interactively with new values of user–defined parameters. A case study of applying VSIS to some UCI repository data sets shows the usefulness of our tool in supporting the exploration of multidimensional and very large data

Viewing Abstract Data as Maps

Abstract From telecommunications and abstractions of the Internet to interconnections of medical papers to on-line social networks, technology has spawned an explosion of data in the form of large attributed graphs and networks. Visualization often serves as an essential first step in understanding such data, when little is known. Unfortunately, visualizing large graphs presents its own set of problems, both technically in terms of clutter and cognitively in terms of unfamiliarity with the graph idiom. In this chapter, we consider viewing such data in the form of geographic maps. This provides a view of the data that naturally allows for reduction of clutter and for presentation in a familiar idiom. We describe some techniques for creating such maps, and consider some of the related technical problems. We also present and discuss various applications of this method to real data.