Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Frequent pattern mining and knowledge indexing based on zero-suppressed BDDs
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Symmetric item set mining based on zero-suppressed BDDs
DS'06 Proceedings of the 9th international conference on Discovery Science
VSOP (valued-sum-of-products) calculator for knowledge processing based on zero-suppressed BDDs
Proceedings of the 2005 international conference on Federation over the Web
Efficient database analysis using VSOP calculator based on zero-suppressed BDDs
JSAI'05 Proceedings of the 2005 international conference on New Frontiers in Artificial Intelligence
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Manipulation of large-scale combinatorial data is one of the important fundamental technique for web information retrieval, integration, and mining. In this paper, we propose a new approach based on BDDs (Binary Decision Diagrams) for database analysis problems. BDDs are graph-based representation of Boolean functions, now widely used in system design and verification area. Here we focus on Zero-suppressed BDDs (ZBDDs), a special type of BDDs, which are suitable for handling large-scale sets of combinations. Using ZBDDs, we can implicitly enumerate combinatorial item set data and efficiently compute set operations over the ZBDDs. We present some encouraging experimental results of frequent item set mining problem for practical benchmark examples, some of which have never been generated by previous method.