Sequence mining in categorical domains: incorporating constraints
Proceedings of the ninth international conference on Information and knowledge management
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
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
SPIRIT: Sequential Pattern Mining with Regular Expression Constraints
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A new algorithm for gap constrained sequence mining
Proceedings of the 2004 ACM symposium on Applied computing
An Efficient Algorithm for Mining Frequent Sequences by a New Strategy without Support Counting
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Efficient mining of sequential patterns with time constraints by delimited pattern growth
Knowledge and Information Systems
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Sequential pattern mining is to find out all the frequent sub-sequences in a sequence database. In order to have more accurate results, constraints in addition to the support threshold need to be specified in the mining. Time-constraints cannot be managed by retrieving patterns because the support computation of patterns must validate the time attributes for every data sequence in the mining process. In this paper, we propose a memory time-indexing approach (called METISP) to discover sequential patterns with time constraints including minimum/maximum/exact gaps, sliding window, and duration. METISP scans the database into memory and constructs time-index sets for effective processing. Utilizing the index sets and the pattern-growth strategy, METISP efficiently mines the desired patterns without generating any candidate or sub-database. The comprehensive experiments show that METISP outperforms GSP and DELISP in the discovery of time-constrained sequential patterns, even with low support thresholds and very large databases.