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IEEE Transactions on Computers
Zero-suppressed BDDs for set manipulation in combinatorial problems
DAC '93 Proceedings of the 30th international Design Automation Conference
Verification of arithmetic circuits with binary moment diagrams
DAC '95 Proceedings of the 32nd annual ACM/IEEE Design Automation Conference
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on Data Engineering
Solving Graph Optimization Problems with ZBDDs
EDTC '97 Proceedings of the 1997 European conference on Design and Test
Using transposition for pattern discovery from microarray data
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
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Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Carpenter: finding closed patterns in long biological datasets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Frequent Closed Patterns in Microarray Data
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
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
Top-Down Mining of Interesting Patterns from Very High Dimensional Data
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
LCM ver.3: collaboration of array, bitmap and prefix tree for frequent itemset mining
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
SWOD '07 Proceedings of the 2007 IEEE International Workshop on Databases for Next Generation Researchers
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
Mining influential attributes that capture class and group contrast behaviour
Proceedings of the 17th ACM conference on Information and knowledge management
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Mining frequent patterns such as frequent itemsets is a core operation in many important data mining tasks, such as in association rule mining. Mining frequent itemsets in high-dimensional datasets is challenging, since the search space is exponential in the number of dimensions and the volume of patterns can be huge. Many of the state-of-the-art techniques rely upon the use of prefix trees (e.g. FP-trees) which allow nodes to be shared among common prefix paths. However, the scalability of such techniques may be limited when handling high dimensional datasets. The purpose of this paper is to analyse the behaviour of mining frequent itemsets when instead of a tree data structure, a canonical directed acyclic graph namely Zero Suppressed Binary Decision Diagram (ZBDD) is used. Due to its compactness and ability to promote node reuse, ZBDD has proven very effective in other areas of computer science, such as boolean SAT solvers. In this paper, we show how ZBDDs can be used to mine frequent itemsets (and their common varieties). We also introduce a weighted variant of ZBDD which allows a more efficient mining algorithm to be developed. We provide an experimental study concentrating on high dimensional biological datasets, and identify indicative situations where a ZBDD technology can be superior over the prefix tree based technique.