Knowledge Compilation for Itemset Mining

Authors:
Hadrien Cambazard;Tarik Hadzic;Barry O'Sullivan
Affiliations:
Cork Constraint Computation Centre, University College Cork, Ireland. {h.cambazard, t.hadzic, b.osullivan}@4c.ucc.ie;Cork Constraint Computation Centre, University College Cork, Ireland. {h.cambazard, t.hadzic, b.osullivan}@4c.ucc.ie;Cork Constraint Computation Centre, University College Cork, Ireland. {h.cambazard, t.hadzic, b.osullivan}@4c.ucc.ie
Venue:
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Year:
2010

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Abstract

We present a novel approach to itemset mining whereby the set of all itemsets are compiled into a compact form, closely related to binary decision diagrams. While there were previous attempts to utilize decision diagrams for storing the set of frequent itemsets this is the first approach that does not rely on backtrack search to generate such a set. Our empirical evaluation demonstrates that our approach is complementary to current approaches.