Discovering patterns in sequences of events
Artificial Intelligence
Fast text searching: allowing errors
Communications of the ACM
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Efficient enumeration of frequent sequences
Proceedings of the seventh international conference on Information and knowledge management
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
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
ICDE '95 Proceedings of the Eleventh 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
On the Discovery of Weak Periodicities in Large Time Series
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Mining regular patterns in data streams
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
A parallel algorithm for mining multiple partial periodic patterns
Information Sciences: an International Journal
Efficient mining regularly frequent patterns in transactional databases
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
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In sequential pattern mining, the support of the sequential pattern for the transaction database is defined only by the fraction of the customers supporting this sequence, which is known as the customer support. In this paper, a new parameter is introduced for each customer, called as repetition support, as an additional constraint to specify the minimum number of repetitions of the patterns by each customer. We call the patterns discovered using this technique as cyclically repeated patterns. The additional parameter makes the new mining technique more efficient and also helps discovering more useful patterns by reducing the number of patterns searched. Also, ordinary sequential pattern mining can be represented as a special case of the cyclically repeated pattern mining. In this paper, we introduce the concept of mining cyclically repeated patterns, we describe the related algorithms, and at the end of the paper we give some performance results.