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Published in:
2010 | OriginalPaper | Chapter
Cube Based Summaries of Large Association Rule Sets
Authors : Marie Ndiaye, Cheikh T. Diop, Arnaud Giacometti, Patrick Marcel, Arnaud Soulet
Published in: Advanced Data Mining and Applications
Publisher: Springer Berlin Heidelberg
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A major problem when dealing with association rules post-processing is the huge amount of extracted rules. Several approaches have been implemented to summarize them. However, the obtained summaries are generally difficult to analyse because they suffer from the lack of navigational tools. In this paper, we propose a novel method for summarizing large sets of association rules. Our approach enables to obtain from a rule set, several summaries called Cube Based Summaries (CBSs). We show that the CBSs can be represented as cubes and we give an overview of OLAP navigational operations that can be used to explore them. Moreover, we define a new quality measure called homogeneity, to evaluate the interestingness of CBSs. Finally, we propose an algorithm that generates a relevant CBS w.r.t. a quality measure, to initialize the exploration. The evaluation of our algorithm on benchmarks proves the effectiveness of our approach.