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A Probabilistic Logic-based Framework for Characterizing Knowledge Discovery in Databases

  • Ying Xie
  • Vijay V. Raghavan
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 6)

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 “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. 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 the given data. By customizing the parameters and providing more constraints, users can guide the knowledge discovery process to obtain a specific subset of all previously unknown and potentially useful patterns, in order to satisfy their current needs.

Keywords

Association Rule Knowledge Discovery Probability Logic Concept Lattice Reference Class 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Authors and Affiliations

  • Ying Xie
    • 1
  • Vijay V. Raghavan
    • 1
  1. 1.The Center for Advanced Computer StudiesUniversity of LouisianaLafayette

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