Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 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: 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
Mining Sequential Patterns with Negative Conclusions
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Incremental mining of sequential patterns: Progress and challenges
Intelligent Data Analysis
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Mining of sequential patterns is an important issue among the various data mining problems. The problem of incremental mining of sequential patterns deserves as much attention. In this paper, we consider the problem of the incremental updating of sequential pattern mining when some transactions and/or data sequences are deleted from the original sequence database. We present a new algorithm, called IU_D, for mining frequent sequences so as to make full use of information obtained during an earlier mining process for reducing the cost of finding new sequential patterns in the updated database. The results of our experiment show that the algorithm performs significantly faster than the naive approach of mining the entire updated database from scratch.