Discovering Patterns from Large and Dynamic Sequential Data
Journal of Intelligent Information Systems
FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
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
Mining Algorithms for Sequential Patterns in Parallel: Hash Based Approach
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Constraint-based sequential pattern mining: the consideration of recency and compactness
Decision Support Systems
Softening the blow of frequent sequence analysis: soft constraints and temporal accuracy
International Journal of Web Engineering and Technology
On mining multi-time-interval sequential patterns
Data & Knowledge Engineering
Knowledge gathering of fuzzy multi-time-interval sequential patterns
Information Sciences: an International Journal
A new algorithm for fast discovery of maximal sequential patterns in a document collection
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
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An active research in data mining is the discovery of sequential patterns, which finds all frequent sub-sequences in a sequence database. Most of the studies specify no time constraints such as maximum/minimum gaps between adjacent elements of a pattern in the mining so that the resultant patterns may be uninteresting. In addition, a data sequence containing a pattern is rigidly defined as only when each element of the pattern is contained in a distinct element of the sequence. This limitation might lose useful patterns for some applications because sometimes items of an element might be spread across adjoining elements within a specified time period or time window. Therefore, we propose a pattern-growth approach for mining the generalized sequential patterns. Our approach features in reducing the size of sub-databases by bounded and windowed projection techniques. Bounded projections keep only time-gap valid sub-sequences and windowed projections save non-redundant sub-sequences satisfying the sliding time window constraint. Furthermore, the delimited growth technique directly generates constraint-satisfactory patterns and speeds up the growing process. The empirical evaluations show that the proposed approach has good linear scalability and outperforms the well-known GSP algorithm in the discovery of generalized sequential patterns.