Fast discovery of association rules
Advances in knowledge discovery and data mining
Efficient enumeration of frequent sequences
Proceedings of the seventh international conference on Information and knowledge management
TRIPs: an integrated intelligent problem-solving assistant
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Knowledge Discovery from Telecommunication Network Alarm Databases
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining sequential patterns with constraints in large databases
Proceedings of the eleventh international conference on Information and knowledge management
Web site mining: a new way to spot competitors, customers and suppliers in the world wide web
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Improving spatial locality of programs via data mining
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Temporal web log mining using olap techniques
ICCMSE '03 Proceedings of the international conference on Computational methods in sciences and engineering
A new algorithm for gap constrained sequence mining
Proceedings of the 2004 ACM symposium on Applied computing
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
Parallel tree-projection-based sequence mining algorithms
Parallel Computing
Mining, indexing, and querying historical spatiotemporal data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
The complexity of mining maximal frequent itemsets and maximal frequent patterns
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
IncSpan: incremental mining of sequential patterns in large database
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Chopper: efficient algorithm for tree mining
Journal of Computer Science and Technology
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Scalable sequential pattern mining for biological sequences
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Mining block correlations to improve storage performance
ACM Transactions on Storage (TOS)
CP-Miner: a tool for finding copy-paste and related bugs in operating system code
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Incremental and interactive mining of web traversal patterns
Information Sciences: an International Journal
Mining sequential patterns in the B2B environment
Journal of Information Science
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In this paper we present SPADE, a new algorithm for fast discovery of Sequential Patterns. The existing solutions to this problem make repeated database scans, and use complex hash structures which have poor locality. SPADE utilizes combinatorial properties to decompose the original problem into smaller sub-problems, that can be independently solved in main-memory using efficient lattice search techniques, and using simple join operations. All sequences are discovered in only three database scans. Experiments show that SPADE outperforms the best previous algorithm by a factor of two, and by an order of magnitude with some pre-processed data. It also has linear scalability with respect to the number of input-sequences, and a number of other database parameters. Finally, we discuss how the results of sequence mining can be applied in a real application domain.