Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
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
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
A General Incremental Technique for Maintaining Discovered Association Rules
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
An Adaptive Algorithm for Incremental Mining of Association Rules
DEXA '98 Proceedings of the 9th International Workshop on Database and Expert Systems Applications
A new incremental data mining algorithm using pre-large itemsets
Intelligent Data Analysis
A flexible and efficient sequential pattern mining algorithm
International Journal of Intelligent Information and Database Systems
Using TF-IDF to hide sensitive itemsets
Applied Intelligence
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Mining sequential patterns from temporal transaction databases attempts to find customer behavior models and to assist managers in making correct and effective decisions. The sequential patterns discovered may, however, become invalid or inappropriate when databases are updated. Conventional approaches may re-mine entire databases to get correct sequential patterns for maintenance. However, when a database is massive in size, this will require considerable computation time. In the past, Lin and Lee proposed an incremental mining algorithm for maintenance of sequential patterns as new records were inserted. In addition to record insertion, record deletion is also commonly seen in real-world applications. Processing record deletion is, however, different from processing record insertion. The former can even be thought of the contrary of the latter. In this paper, we thus attempt to design an effective maintenance algorithm for sequential patterns as records are deleted. Our proposed algorithm utilizes previously discovered large sequences in the maintenance process, thus reducing numbers of rescanning databases. In addition, rescanning requirement depends on decreased numbers of customers, which are usually zero when numbers of deleted records are not large. This characteristic is especially useful for dynamic database mining.