SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
A tree projection algorithm for generation of frequent item sets
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
KDD-Cup 2000 organizers' report: peeling the onion
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Efficient Mining of Intertransaction Association Rules
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A template model for multidimensional inter-transactional association rules
The VLDB Journal — The International Journal on Very Large Data Bases
Efficient data mining for calling path patterns in GSM networks
Information Systems
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure
IEEE Transactions on Knowledge and Data Engineering
Mining spatial association rules in image databases
Information Sciences: an International Journal
An efficient algorithm for mining frequent inter-transaction patterns
Information Sciences: an International Journal
Frequent Closed Sequence Mining without Candidate Maintenance
IEEE Transactions on Knowledge and Data Engineering
An efficient algorithm for mining closed inter-transaction itemsets
Data & Knowledge Engineering
Top-down mining of frequent closed patterns from very high dimensional data
Information Sciences: an International Journal
Mining inter-sequence patterns
Expert Systems with Applications: An International Journal
On Mining Repeating Pattern with Gap Constraint
HPCC '09 Proceedings of the 2009 11th IEEE International Conference on High Performance Computing and Communications
Mining closed flexible patterns in time-series databases
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Mining association rules with multi-dimensional constraints
Journal of Systems and Software
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Inter-sequence pattern mining can find associations across several sequences in a sequence database, which can discover both a sequential pattern within a transaction and sequential patterns across several different transactions. However, inter-sequence pattern mining algorithms usually generate a large number of recurrent frequent patterns. We have observed mining closed inter-sequence patterns instead of frequent ones can lead to a more compact yet complete result set. Therefore, in this paper, we propose a model of closed inter-sequence pattern mining and an efficient algorithm called CISP-Miner for mining such patterns, which enumerates closed inter-sequence patterns recursively along a search tree in a depth-first search manner. In addition, several effective pruning strategies and closure checking schemes are designed to reduce the search space and thus accelerate the algorithm. Our experiment results demonstrate that the proposed CISP-Miner algorithm is very efficient and outperforms a compared EISP-Miner algorithm in most cases.