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.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
Author information
Authors and Affiliations
Editor information
Rights and permissions
About this chapter
Cite this chapter
Xie, Y., V. Raghavan, V. A Probabilistic Logic-based Framework for Characterizing Knowledge Discovery in Databases. In: Young Lin, T., Ohsuga, S., Liau, CJ., Hu, X., Tsumoto, S. (eds) Foundations of Data Mining and knowledge Discovery. Studies in Computational Intelligence, vol 6. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11498186_5
Download citation
DOI: https://doi.org/10.1007/11498186_5
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26257-2
Online ISBN: 978-3-540-32408-9
eBook Packages: EngineeringEngineering (R0)