Efficient enumeration of frequent sequences
Proceedings of the seventh international conference on Information and knowledge management
Mining sequential patterns with constraints in large databases
Proceedings of the eleventh international conference on Information and knowledge management
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
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
The PSP Approach for Mining Sequential Patterns
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Efficient Algorithms for Incremental Update of Frequent Sequences
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
CLOSET+: searching for the best strategies for mining frequent closed itemsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Information Systems - Databases: Creation, management and utilization
IncSpan: incremental mining of sequential patterns in large database
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Frequent Closed Sequence Mining without Candidate Maintenance
IEEE Transactions on Knowledge and Data Engineering
Maintenance of discovered sequential patterns for record deletion
Intelligent Data Analysis
Sequential pattern mining with time intervals
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Discovering fuzzy time-interval sequential patterns in sequence databases
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hi-index | 0.00 |
Sequential pattern mining has gathered great attention in recent years due to its broad applications. Most of the existing methods are in two categories: 1) candidate-generation-and-test approaches such as GSP, requiring multiple database scans, 2) pattern-growth approaches such as PrefixSpan, scanning the projected database which may be several times larger than the original database. Methods from both categories must set minimum support thresholds in advance. To remedy the problems, we propose a new approach, Fast Sequential Pattern Enumeration (FSPE), to mine sequential patterns without the need to predetermine the minimum support threshold. The FSPE scans the transaction database only once to enumerate all candidate sequences with efficient indexing of their support counters. Using our approach one can easily produce meaningful rules for any item that appears at least once in the sequence database.