Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
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
SPIRIT: Sequential Pattern Mining with Regular Expression Constraints
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th 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
Mining Top.K Frequent Closed Patterns without Minimum Support
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Data mining for path traversal patterns in a web environment
ICDCS '96 Proceedings of the 16th International Conference on Distributed Computing Systems (ICDCS '96)
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
CanTree: a canonical-order tree for incremental frequent-pattern mining
Knowledge and Information Systems
A new concise representation of frequent itemsets using generators and a positive border
Knowledge and Information Systems
Efficient Discovery of Top-K Minimal Jumping Emerging Patterns
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Pattern Taxonomy Mining for Information Filtering
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Discovering Compatible Top-K Theme Patterns from Text Based on Users' Preferences
PAISI '09 Proceedings of the Pacific Asia Workshop on Intelligence and Security Informatics
Mining sequential patterns across multiple sequence databases
Data & Knowledge Engineering
Mining weighted sequential patterns in a sequence database with a time-interval weight
Knowledge-Based Systems
TOPSIL-Miner: an efficient algorithm for mining top-K significant itemsets over data streams
Knowledge and Information Systems
Towards bounding sequential patterns
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast mining of non-derivable episode rules in complex sequences
MDAI'11 Proceedings of the 8th international conference on Modeling decisions for artificial intelligence
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
On mining clinical pathway patterns from medical behaviors
Artificial Intelligence in Medicine
Mining top-k association rules
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
International Journal of Intelligent Information and Database Systems
Fast mining Top-Rank-k frequent patterns by using Node-lists
Expert Systems with Applications: An International Journal
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Sequential pattern mining has been studied extensively in the data mining community. Most previous studies require the specification of a min_support threshold for mining a complete set of sequential patterns satisfying the threshold. However, in practice, it is difficult for users to provide an appropriate min_support threshold. To overcome this difficulty, we propose an alternative mining task: mining top-k frequent closed sequential patterns of length no less than min_ℓ, where k is the desired number of closed sequential patterns to be mined and min_ℓ is the minimal length of each pattern. We mine the set of closed patterns because it is a compact representation of the complete set of frequent patterns. An efficient algorithm, called TSP, is developed for mining such patterns without min_support. Starting at (absolute) min_support=1, the algorithm makes use of the length constraint and the properties of top-k closed sequential patterns to perform dynamic support raising and projected database pruning. Our extensive performance study shows that TSP has high performance. In most cases, it outperforms the efficient closed sequential pattern-mining algorithm, CloSpan, even when the latter is running with the best tuned min_support threshold. Thus, we conclude that, for sequential pattern mining, mining top-k frequent closed sequential patterns without min_support is more preferable than the traditional min_support-based mining.