Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Sequence mining in categorical domains: incorporating constraints
Proceedings of the ninth international conference on Information and knowledge management
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Mining Frequent Item Sets with Convertible Constraints
Proceedings of the 17th 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
Discovering User Behavior Patterns in Personalized Interface Agents
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
Looking for monotonicity properties of a similarity constraint on sequences
Proceedings of the 2006 ACM symposium on Applied computing
Softening the blow of frequent sequence analysis: soft constraints and temporal accuracy
International Journal of Web Engineering and Technology
Effective next-items recommendation via personalized sequential pattern mining
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part II
S2MP: similarity measure for sequential patterns
AusDM '08 Proceedings of the 7th Australasian Data Mining Conference - Volume 87
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Many practical applications are related to frequent sequential pattern mining, ranging from Web Usage Mining to Bioinformatics. To ensure an appropriate extraction cost for useful mining tasks, a key issue is to push the user-defined constraints deep inside the mining algorithms. In this paper, we study the search for frequent sequential patterns that are also similar to an user-defined reference pattern. While the effective processing of the frequency constraints is well-understood, our contribution concerns the identification of a relaxation of the similarity constraint into a convertible anti-monotone constraint. Both constraints are then used to prune the search space during a levelwise search. Preliminary experimental validations have confirmed the algorithm efficiency.