Min-Max itemset trees for dense and categorical datasets

Authors:
Jennifer Lavergne;Ryan Benton;Vijay V. Raghavan
Affiliations:
The Center for Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, LA;The Center for Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, LA;The Center for Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, LA
Venue:
ISMIS'12 Proceedings of the 20th international conference on Foundations of Intelligent Systems
Year:
2012

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Abstract

The itemset tree data structure is used in targeted association mining to find rules within a user's sphere of interest. In this paper, we propose two enhancements to the original unordered itemset trees. The first enhancement consists of sorting all nodes in lexical order based upon the itemsets they contain. In the second enhancement, called the Min-Max Itemset Tree, each node was augmented with minimum and maximum values that represent the range of itemsets contained in the children below. For demonstration purposes, we provide a comprehensive evaluation of the effects of the enhancements on the itemset tree querying process by performing experiments on sparse, dense, and categorical datasets.