Behavior-based clustering and analysis of interestingness measures for association rule mining

Caroline V. Tew; Christophe Giraud-Carrier; Kesler Tanner; Scott H. Burton

doi:10.1007/s10618-013-0326-x

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Behavior-based clustering and analysis of interestingness measures for association rule mining

Caroline V. Tew Christophe Giraud-Carrier Kesler Tanner Scott H. Burton

Year: 2013 Journal: Data Mining and Knowledge Discovery Vol: 28 (4)Pages: 1004-1045 Publisher: Springer Science+Business Media

DOI: 10.1007/s10618-013-0326-x

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Keywords:

Ranking (information retrieval) Association rule learning Cluster analysis Computer science Data mining Scope (computer science) Artificial intelligence

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Data Mining Algorithms and Applications

Physical Sciences → Computer Science → Information Systems

Rough Sets and Fuzzy Logic

Physical Sciences → Computer Science → Computational Theory and Mathematics

Natural Language Processing Techniques

Physical Sciences → Computer Science → Artificial Intelligence

Behavior-based clustering and analysis of interestingness measures for association rule mining

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