Balachandran, KrishnamohanBuzydlowski, JanDworman, GarettKimbrough, Steven. OShafer, TateVachula, William J2023-05-232017-06-0219992016-07-28https://repository.upenn.edu/handle/20.500.14332/42256The 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.This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Management Information Systems on 02 Dec 2015, available online: http://wwww.tandfonline.com/10.1080/07421222.1999.11518232data miningdata visualitionhypotheses explorationknowledge discovery in databasesOLAPprediction analysisDatabases and Information SystemsOther Computer SciencesQuantitative, Qualitative, Comparative, and Historical MethodologiesStatistical TheoryArticle