Efficient enumeration of frequent sequences
Proceedings of the seventh international conference on Information and knowledge management
Sequence mining in categorical domains: incorporating constraints
Proceedings of the ninth 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
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
A new algorithm for gap constrained sequence mining
Proceedings of the 2004 ACM symposium on Applied computing
Efficient strategies for tough aggregate constraint-based sequential pattern mining
Information Sciences: an International Journal
Fast discovery of sequential patterns in large databases using effective time-indexing
Information Sciences: an International Journal
Hi-index | 0.00 |
Sequential pattern mining has now become an important data mining problem. For many practical applications, the users may be only interested in those sequential patterns satisfying some constraints expressing their interest. The proposed constraints in general can be categorized into four classes, among which monotony and tough constraints are the most difficult ones to be processed. However, many of the available algorithms are proposed for some special constraints based sequential pattern mining. It is thus difficult to be adapted to other classes of constraints. In this paper we propose a new general framework called CBPSAlgm based on the projection-based pattern growth principal. Under this framework, ineffective item pruning strategies are designed and integrated to construct effective algorithms for monotony and tough constraint based sequential pattern mining. Experimental results show that our proposed methods outperform other algorithms.