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Symbolic Data Mining Methods with the Coron Platform

Laszlo Szathmary 1
1 ORPAILLEUR - Knowledge representation, reasonning
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : The main topic of this thesis is knowledge discovery in databases (KDD). More precisely, we have investigated two of the most important tasks of KDD today, namely itemset extraction and association rule generation. Throughout our work we have borne in mind that our goal is to find interesting association rules from various points of view: for efficient mining purposes, for minimizing the set of extracted rules and for finding intelligible (and easily interpretable) knowledge units. We have developed and adapted specific algorithms in order to achieve this goal.
The main contributions of this thesis are: (1) We have developed and adapted algorithms for finding minimal non-redundant association rules; (2) We have defined a new basis for association rules called Closed Rules; (3) We have investigated an important but relatively unexplored field of KDD namely the extraction of rare itemsets and rare association rules; (4) We have packaged our algorithms and a collection of other algorithms along with other auxiliary operations for KDD into a unified software toolkit called Coron.
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Submitted on : Monday, November 3, 2008 - 5:55:35 PM
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  • HAL Id : tel-01754284, version 2



Laszlo Szathmary. Symbolic Data Mining Methods with the Coron Platform. Software Engineering [cs.SE]. Université Henri Poincaré - Nancy 1, 2006. English. ⟨NNT : 2006NAN10159⟩. ⟨tel-01754284v2⟩



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