Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
A tree projection algorithm for generation of frequent item sets
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Proceedings of the tenth international conference on Information and knowledge management
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Finding similar regions in many sequences
Journal of Computer and System Sciences - STOC 1999
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Spelling Approximate Repeated or Common Motifs Using a Suffix Tree
LATIN '98 Proceedings of the Third Latin American Symposium on Theoretical Informatics
Top Down FP-Growth for Association Rule Mining
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable sequential pattern mining for biological sequences
Proceedings of the thirteenth ACM international conference on Information and knowledge management
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Correlated protein-DNA interaction (binding cores) between transcription factor (TFs) and transcription factor binding sites (TFBSs) are usually identified by costly 3D structural experiments. To avoid numerous unsuccessful trials, we are motivated to develop a cheap and efficient sequence-based computational method for providing testable novel binding cores with high confidence to accelerate the experiments. Although there are abundant sequence-based motif discovery algorithms, few directly address associating both TF and TFBS core motifs which are both verifiable on 3D structures. In this paper, we formally define the problem of discovering correlated TF-TFBS binding cores, and apply association rule mining techniques over existing real sequence data (TRANSFAC). The proposed algorithm first builds two frequent sequence tree (FS-Tree) structures storing condensed information for association rule mining. Association rules are then generated by depth-first traversal on the structures. FS-Trees have several advantages to support further applications, including efficient calculation of the support and confidence, simple generation of candidate rules, and applicability of effective pruning techniques. As a result, the FS-Trees serve as a useful basis for more general extensions related to biological binding core identification. We tested our algorithm on real sequence data from the biological database TRANSFAC and focus on efficiency comparisons with the recent work employing association rule mining. The rules discovered reveal real TF-TFBS binding cores in independent 3D verifications on Protein Data Bank (PDB).