This 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 practice in OLAP (on-line 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 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.
Balachandran, K.; Buzydlowski, J.; Dworman, G.; Kimbrough, S.O.; Shafer, T.; and Vachula, W.
"MOTC with Examples: An Interactive Aid for Multidimensional Hypothesis Generation,"
Communications of the Association for Information Systems: Vol. 4
, Article 15.
Available at: https://aisel.aisnet.org/cais/vol4/iss1/15