Mining frequent itemsets from multidimensional databases

Authors:
Bay Vo;Bac Le;Thang N. Nguyen
Affiliations:
Faculty of Information Technology, Ho Chi Minh City University of Technology, Vietnam;Faculty of Information Technology University of Science, Ho Chi Minh, Vietnam;California State University Long Beach
Venue:
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
Year:
2011

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Abstract

Mining frequent itemsets (FIs) has been developing in recent years. However, little attention has been paid to efficient methods for mining in multidimensional databases. In this paper, we propose a new method with a supporting structure called AIO-tree (Attributes Itemset Object identifications - tree) for mining FIs from multidimensional databases. This method need not transform the database into the transaction database, and it is based on the intersections of object identifications for fast computing the support of itemsets. We compare our method to dEclat (after transformation to a transaction database) and indeed claim that they are faster than dEclat.