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
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
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Mining Top.K Frequent Closed Patterns without Minimum Support
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
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Automatic Pattern-Taxonomy Extraction for Web Mining
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
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IEEE Transactions on Knowledge and Data Engineering
CP-Miner: Finding Copy-Paste and Related Bugs in Large-Scale Software Code
IEEE Transactions on Software Engineering
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IEEE Transactions on Knowledge and Data Engineering
Horn axiomatizations for sequential data
Theoretical Computer Science
Constraint-based sequential pattern mining: the pattern-growth methods
Journal of Intelligent Information Systems
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Knowledge and Information Systems
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International Journal of Knowledge-based and Intelligent Engineering Systems
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IEEE Transactions on Knowledge and Data Engineering
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ACM SIGKDD Explorations Newsletter - Special issue on data mining for health informatics
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Journal of Computer Science and Technology
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Knowledge-Based Systems
Efficient sequential pattern mining algorithms
AIKED'05 Proceedings of the 4th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering Data Bases
Efficient mining of frequent closed XML query pattern
Journal of Computer Science and Technology
Mining top-k Hot Melody Structures over online music query streams
Pattern Recognition Letters
Efficient algorithms for incremental maintenance of closed sequential patterns in large databases
Data & Knowledge Engineering
A Unified Approach to Web Usage Mining Based on Frequent Sequence Mining
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
A sliding window method for finding top-k path traversal patterns over streaming Web click-sequences
Expert Systems with Applications: An International Journal
Effective database transformation and efficient support computation for mining sequential patterns
Journal of Intelligent Information Systems
Interactive mining of top-K frequent closed itemsets from data streams
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Intelligent sequential mining via alignment: optimization techniques for very large DB
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Mining rough association from text documents for web information gathering
Transactions on rough sets VII
Mining top-k frequent closed itemsets over data streams using the sliding window model
Expert Systems with Applications: An International Journal
TGP: mining top-K frequent closed graph pattern without minimum support
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
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Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
An entropy-based approach for generating multi-dimensional sequential patterns
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
An empirical study on mining sequential patterns in a grid computing environment
Expert Systems with Applications: An International Journal
A two-stage decision model for information filtering
Decision Support Systems
Mining rough association from text documents
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
A pruning technique to discover correlated sequential patterns in retail databases
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Data Mining and Knowledge Discovery
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Sequential pattern mining has been studied extensivelyin data mining community.Most previous studies requirethe specification of a minimum support threshold to performthe mining.However, it is difficult for users to providean appropriate threshold in practice.To overcomethis difficulty, we propose an alternative task: mining top-kfrequent closed sequential patterns of length no less thanmin_l, where k is the desired number of closed sequentialpatterns to be mined, and min_l is the minimum length ofeach pattern.We mine closed patterns since they are compactrepresentations of frequent patterns.We developed an efficient algorithm, called TSP, whichmakes use of the length constraint and the properties of top-kclosed sequential patterns to perform dynamic support-raisingand projected database-pruning.Our extensive performancestudy shows that TSP outperforms the closed sequentialpattern mining algorithm even when the latter isrunning with the best tuned minimum support threshold.