Fast sequential and parallel algorithms for association rule mining: a comparison
Fast sequential and parallel algorithms for association rule mining: a comparison
Discovering Patterns from Large and Dynamic Sequential Data
Journal of Intelligent Information Systems
Efficient enumeration of frequent sequences
Proceedings of the seventh international conference on Information and knowledge management
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
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Online Generation of Association Rules
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
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
The PSP Approach for Mining Sequential Patterns
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Algorithms for Sequential Patterns in Parallel: Hash Based Approach
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Improving the Efficiency of Interactive Sequential Pattern Mining by Incremental Pattern Discovery
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 3 - Volume 3
Online Association Rule Mining
Online Association Rule Mining
Mining spatial association rules in image databases
Information Sciences: an International Journal
EDUA: An efficient algorithm for dynamic database mining
Information Sciences: an International Journal
Efficient mining of weighted interesting patterns with a strong weight and/or support affinity
Information Sciences: an International Journal
Incremental and interactive mining of web traversal patterns
Information Sciences: an International Journal
Interactive mining of frequent itemsets over arbitrary time intervals in a data stream
ADC '08 Proceedings of the nineteenth conference on Australasian database - Volume 75
Fast discovery of sequential patterns in large databases using effective time-indexing
Information Sciences: an International Journal
Efficient single-pass frequent pattern mining using a prefix-tree
Information Sciences: an International Journal
An approach to discovering multi-temporal patterns and its application to financial databases
Information Sciences: an International Journal
Variable support mining of frequent itemsets over data streams using synopsis vectors
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
CSSF-trie structure to mine constraint sequential patterns from progressive database
International Journal of Knowledge Engineering and Data Mining
Incremental mining of sequential patterns: Progress and challenges
Intelligent Data Analysis
Efficient mining of maximal correlated weight frequent patterns
Intelligent Data Analysis
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Sequential pattern mining has become a challenging task in data mining due to its complexity. Essentially, the mining algorithms discover all the frequent patterns meeting the user specified minimum support threshold. However, it is very unlikely that the user could obtain the satisfactory patterns in just one query. Usually the user must try various support thresholds to mine the database for the final desirable set of patterns. Consequently, the time-consuming mining process has to be repeated several times. However, current approaches are inadequate for such interactive mining due to the long processing time required for each query. In order to reduce the response time for each query during the interactive process, we propose a knowledge base assisted mining algorithm for interactive sequence discovery. The proposed approach utilizes the knowledge acquired from each mining process, accumulates the counting information to facilitate efficient counting of patterns, and speeds up the whole interactive mining process. Furthermore, the knowledge base makes possible the direct generation of new candidate sets and the concurrent support counting of variable sized candidates. Even for some queries, due to the pattern information already kept in the knowledge base, database access is not required at all. The conducted experiments show that our approach outperforms GSP, a state-of-the-art sequential pattern mining algorithm, by several order of magnitudes for interactive sequence discovery.