Symmetric item set mining based on zero-suppressed BDDs

Authors:
Shin-ichi Minato
Affiliations:
Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan
Venue:
DS'06 Proceedings of the 9th international conference on Discovery Science
Year:
2006

Citing 6
Cited 1

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Efficient Method of Combinatorial Item Set Analysis Based on Zero-Suppressed BDDs

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An anytime symmetry detection algorithm for ROBDDs

ASP-DAC '06 Proceedings of the 2006 Asia and South Pacific Design Automation Conference
Fast computation of symmetries in Boolean functions

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LCM over ZBDDS: fast generation of very large-scale frequent itemsets using a compact graph-based representation

PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining

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Abstract

In this paper, we propose a method for discovering hidden information from large-scale item set data based on the symmetry of items. Symmetry is a fundamental concept in the theory of Boolean functions, and there have been developed fast symmetry checking methods based on BDDs (Binary Decision Diagrams). Here we discuss the property of symmetric items in data mining problems, and describe an efficient algorithm based on ZBDDs (Zero-suppressed BDDs). The experimental results show that our ZBDD-based symmetry checking method is efficiently applicable to the practical size of benchmark databases.