Mining Multi-Level Frequent Itemsets under Constraints

Details Mining Multi-Level Frequent Itemsets under Constraints Mining Multi-Level Frequent Itemsets under Constraints

2010-12-26

arxiv.org/abs/1012.5546v1

Internatinal Journal of Database Theory and Application, Vol. 3, No. 4, PP. 15-35, December, 2010

Computer Science

Databases

20 pages

Scientific paper

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 levels of 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 and Pruning based Approach.

Affiliated with

Also associated with

No associations

Rating

Mining Multi-Level Frequent Itemsets under Constraints does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Mining Multi-Level Frequent Itemsets under Constraints, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Mining Multi-Level Frequent Itemsets under Constraints will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-22321