Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Mining optimized association rules for numeric attributes
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
IEEE Transactions on Knowledge and Data Engineering
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
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
Incremental Maintenance of Frequent Itemsets in Evidential Databases
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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In the past, we proposed an incremental mining algorithm for maintenance of generalized association rules as new transactions were inserted. Deletion of records in databases is, however, commonly seen in real-world applications. In this paper, we thus attempt to extend our previous approach to solve this issue. The proposed algorithm maintains generalized association rules based on the concept of pre-large itemsets for deleted data. The concept of pre-large itemsets is used to reduce the need for rescanning original databases and to save maintenance costs. The proposed algorithm doesn't need to rescan the original database until a number of records have been deleted. It can thus save much maintenance time.