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Talks & Seminars
Probabilistic Logic-based Characterization of Knowledge Discovery in Databases
Vijay V. Raghavan, Univ. of Louisiana
Date & Time: December 22, 2003 10:30
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Abstract:
From the perspective of knowledge representation and reasoning as well as for the automation of the knowledge discovery process, we argue that a formal logical foundation is needed for KDD and suggest Bacchus' probability logic is a good choice. It is generally accepted that the unique and most important feature of a KDD system lies in its ability to discover previously unknown and potentially useful patterns. Therefore we give a formal definition of "pattern" as well as its determiners, which are "previously unknown" and "potentially useful", by completely staying within the expressiveness of Bacchus' probability logic language. Furthermore, based on this logic, we propose a logic induction operator that defines the process through which all the potentially useful patterns embedded in the given data can be discovered. Hence, general knowledge discovery (independent of any application)is defined to be any process functionally equivalent to the process specified by this logic induction operator with respect to given data. By customizing the parameters and/or providing more constraints, users can guide the knowledge discovery process to obtain a specific subset of the potentially useful patterns that would result from general knowledge discovery, in order to satisfy their current needs.
Speaker Profile:
Vijay V. Raghavan Distinguished Professor of Computer Science The Center for Advanced Computer Studies Univ. of Louisiana, Lafayette Lafayette, LA 70504-4330
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