Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An efficient algorithm to update large itemsets with early pruning
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient search for association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
An efficient approach to discovering knowledge from large databases
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Mining the Smallest Association Rule Set for Predictions
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th 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)
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An Informative Rule Set (IRS) is the smallest subset of an association rule set such that it has the same prediction sequence by confidence priority [9]. The problem of maintenance of IRS is a process by which, given a transaction database and its IRS, when the database receives insertion, deletion, or modification, we wish to maintain the IRS as efficiently as possible. Based on the Fast UPdating technique (FUP) [5] for the updating of discovered association rules, we propose here two algorithms to update the discovered IRS when the database is updated by insertion and deletion respectively. Numerical comparisons with the non-incremental informative rule set approach show that our proposed techniques require less computation time, due to less database scanning and less number of candidate rules generated.