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CORR
2010
Springer

Mining Multi-Level Frequent Itemsets under Constraints

9 years 1 months ago
Mining Multi-Level Frequent Itemsets under Constraints
Mining association rules is a task of data mining, which extracts knowledge in the form of significant implication relation of useful items (objects) from a database. Mining multilevel association rules uses concept hierarchies, also called taxonomies and defined as relations of type 'is-a' between objects, to extract rules that items belong to different f abstraction. These rules are more useful, more refined and more interpretable by the user. Several algorithms have been proposed in the literature to discover the multilevel association rules. In this article, we are interested in the problem of discovering multi-level frequent itemsets under constraints, involving the user in the research process. We proposed a technique for modeling and interpretation of constraints in a context of use of concept hierarchies. Three approaches for discovering multi-level frequent itemsets under constraints were proposed and discussed: Basic approach, "Test and Generate" approach...
Mohamed Salah Gouider, Amine Farhat
Added 01 Mar 2011
Updated 01 Mar 2011
Type Journal
Year 2010
Where CORR
Authors Mohamed Salah Gouider, Amine Farhat
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