Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Zero-suppressed BDDs for set manipulation in combinatorial problems
DAC '93 Proceedings of the 30th international Design Automation Conference
A new viewpoint on two-level logic minimization
DAC '93 Proceedings of the 30th international Design Automation Conference
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Binary decision diagrams and applications for VLSI CAD
Binary decision diagrams and applications for VLSI CAD
Fast discovery of association rules
Advances in knowledge discovery and data mining
On the properties of combination set operations
Information Processing Letters
Efficient mining of association rules using closed itemset lattices
Information Systems
Communications of the ACM
Modern Information Retrieval
Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries
Data Mining and Knowledge Discovery
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on Data Engineering
Mining All Non-derivable Frequent Itemsets
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Efficient Method of Combinatorial Item Set Analysis Based on Zero-Suppressed BDDs
WIRI '05 Proceedings of the International Workshop on Challenges in Web Information Retrieval and Integration
Itemset support queries using frequent itemsets and their condensed representations
DS'06 Proceedings of the 9th international conference on Discovery Science
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Knowledge Compilation for Itemset Mining
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
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Frequent pattern mining is one of the fundamental techniques for knowledge discovery and data mining. During the last decade, several efficient algorithms for frequent pattern mining have been presented, but most algorithms have focused on enumerating the patterns that satisfy the given conditions, considering the storage and indexing of the pattern results for efficient inductive analysis to be a separate issue. In this paper, we propose a fast algorithm for extracting all/maximal frequent patterns from transaction databases and simultaneously indexing a huge number of patterns using Zero-suppressed Binary Decision Diagrams (ZBDDs). Our method is comparably fast as existing state-ofthe-art algorithms and not only enumerates/lists the patterns but also compactly indexes the output data in main memory. After mining, the pattern results can be analyzed efficiently by using algebraic operations. BDD-based data structures have previously been used successfully in VLSI logic design, but our method is the first practical application of BDD-based techniques in the data mining area.