SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
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
The PSP Approach for Mining Sequential Patterns
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Managing Interesting Rules in Sequence Mining
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
BIDE: Efficient Mining of Frequent Closed Sequences
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
TSP: Mining top-k closed sequential patterns
Knowledge and Information Systems
Efficient mining of frequent sequence generators
Proceedings of the 17th international conference on World Wide Web
Minimum description length principle: generators are preferable to closed patterns
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Prism: An effective approach for frequent sequence mining via prime-block encoding
Journal of Computer and System Sciences
PBFMCSP: Prefix Based Fast Mining of Closed Sequential Patterns
ACT '09 Proceedings of the 2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies
Non-Redundant Sequential Association Rule Mining and Application in Recommender Systems
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Interestingness measures for association rules: Combination between lattice and hash tables
Expert Systems with Applications: An International Journal
Classification based on association rules: A lattice-based approach
Expert Systems with Applications: An International Journal
MSGPs: a novel algorithm for mining sequential generator patterns
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part II
| Hi-index | 0.00 |
Sequential generator patterns and closed sequential patterns play an important role in data mining tasks. They are proposed to address difficult problems in mining sequential pattern and have often been used together to generate non-redundant rules. Based on their important role, this paper proposes an efficient algorithm called CloGen for mining closed sequential patterns and their minimal sequential generator patterns. The CloGen algorithm uses the parent-child relationship on prefix tree structure and inserts fields into each node on prefix tree to determine whether that is a minimal sequential generator pattern or closed sequential pattern. Experimental results show that the performance runtime of CloGen algorithm is much faster than that of other algorithms by more than one order of magnitude.