MOTC: an aid to multidimensional hypothesis generation

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 spaces 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 practice in OLAP (online analytical processing) 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 upon the specific assumptions the user is willing to make. As such, it provides a very useful framework for exploring the hypothesis space in a KDD context. This paper illustrates these points with an extensive discussion of MOTC.