A database perspective on knowledge discovery
Communications of the ACM
Using a knowledge cache for interactive discovery of association rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Incremental and interactive sequence mining
Proceedings of the eighth international conference on Information and knowledge management
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
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
Incremental Refinement of Mining Queries
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
Using Condensed Representations for Interactive Association Rule Mining
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Mining Sequential Alarm Patterns in a Telecommunication Database
DBTel '01 Proceedings of the VLDB 2001 International Workshop on Databases in Telecommunications II
A complete chronicle discovery approach: application to activity analysis
Expert Systems: The Journal of Knowledge Engineering
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Data mining is an interactive and iterative process. It is very likely that a user will execute a series of similar queries differing in pattern constraints and mining parameters, before he or she gets satisfying results. Unfortunately, data mining algorithms currently available suffer from long processing times, which is unacceptable in case of interactive mining. In this paper we discuss efficient processing of sequential pattern queries utilizing cached results of other sequential pattern queries. We analyze differences between sequential pattern queries and propose algorithms that in many cases can be used instead of time-consuming mining algorithms.