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
A fast distributed algorithm for mining association rules
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
TAR: Temporal Association Rules on Evolving Numerical Attributes
Proceedings of the 17th 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
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th 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)
IncSpan: incremental mining of sequential patterns in large database
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Enhancing SWF for incremental association mining by itemset maintenance
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
An efficient technique for incremental updating of association rules
International Journal of Hybrid Intelligent Systems
An efficient algorithm for incremental mining of temporal association rules
Data & Knowledge Engineering
Fuzzy association rule mining for community crime pattern discovery
ACM SIGKDD Workshop on Intelligence and Security Informatics
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In recent years, the concept of temporal association rule (TAR) has been introduced in order to solve the problem on handling time series by including time expressions into association rules. In real life situations, temporal databases are often appended or updated. Rescanning the complete database every time is impractical while existing incremental mining techniques cannot deal with temporal association rules. In this paper, we propose an incremental algorithm for maintaining temporal association rules with numerical attributes by using the negative border method. The new algorithm has been implemented to support the discoveries of crime patterns in a district of Hong Kong. We have also experimented with another real life database of courier records of a shipping company. The preliminary results show a significant improvement over rerunning TAR algorithm.