A theoretical framework for knowledge discovery in databases based on probabilistic logic

Authors:
Ying Xie;Vijay V. Raghavan
Affiliations:
The Center for Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, LA;The Center for Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, LA
Venue:
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Year:
2003

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Abstract

In order to further improve the KDD process in terms of both the degree of automation achieved and types of knowledge discovered, we argue that a formal logical foundation is needed and suggest that Bacchus' probability logic is a good choice. By completely staying within the expressiveness of Bacchus' probability logic language, we give formal definitions of a "pattern" as well as its determiners, which are "previously unknown" and "potentially useful". These definitions provide a sound foundation to overcome several deficiencies of current KDD systems with respect to novelty and usefulness judgment. Furthermore, based on this logic, we propose a logic induction operator that defines a standard process through which all the potentially useful patterns embedded in the given data can be discovered. This logic induction operator provides a formal characterization of the "discovery" process itself.