An associative information visualizer

IEEE Symposium on Information Visualization, INFOVIS 2004, p. r8

MOTC: An Interactive Aid for Multidimensional Hypothesis Generatio

The paper reports on conceptual development in the areas of database mining and knowledge discovery in databases (KDD). Our efforts have also led to a prototype implementation, called MOTC, for exploring hypothesis space in large and complex data sets. Our KDD conceptual development rests on two main principles. First, we use the crosstab representation for working with qualitative data. This is by now standard in on-line analytical processing (OLAP) applications, and we reaffirm it with additional reasons. Second, and innovatively, we use prediction analysis as a measure of goodness for hypotheses. Prediction analysis is an established statistical technique for analysis of associations among qualitative variables. It generalizes and subsumes a large number of other such measures of association, depending on specific assumptions the user is willing to make. As such, it provides a very useful framework for exploring hypothesis space in a KDD context. The paper illustrates these points with an extensive discussion of MOTC

Comparison of self-organizing maps and pathfinder networks for the mapping of co-cited authors

To my father. ii Acknowledgements Howard D. White, PhD, thesis advisor, who never treated me as a student, but from whom I learned more than any other teacher. The distinguished members of my committee: • Xia Lin, PhD, who treated me as a colleague and friend. • Katherine W. McCain, PhD, who always knew what to do. • Richard Heiberger, PhD, who showed me fascinating new worlds. • Stephen Kimbrough, PhD, for his invaluable contribution. J. Jeffery Hand, Linda Marion, Joan Lussky for their camaraderie. All of the experts involved with this study who were more than generous with their time and expertise. Cheryl L. Berringer for her emotional support, without which none of this would have been possible