Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 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
Efficient enumeration of frequent sequences
Proceedings of the seventh international conference on Information and knowledge management
Incremental and interactive sequence mining
Proceedings of the eighth international conference on Information and knowledge management
Efficient Data Mining for Path Traversal Patterns
IEEE Transactions on Knowledge and Data Engineering
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Pincer Search: A New Algorithm for Discovering the Maximum Frequent Set
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
SPIRIT: Sequential Pattern Mining with Regular Expression Constraints
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
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This paper studies the problem of mining frequent sequences in transactional databases. In [1], Agrawal and Srikant proposed the AprioriAll algorithm for extracting frequently occurring sequences. AprioriAll is an iterative algorithm. It scans the database a number of times depending on the length of the longest frequent sequences in the database. The I/O cost is thus substantial if the database contains very long frequent sequences. In this paper, we propose a new I/O-efficient algorithm FFS. Experiment results show that FFS saves I/O cost significantly compared with AprioriAll. The I/O saving is obtained at a cost of a mild overhead in CPU cost.