Frequent itemset creation using sequence association rule
PDCN '08 Proceedings of the IASTED International Conference on Parallel and Distributed Computing and Networks
An efficient algorithm for incremental mining of temporal association rules
Data & Knowledge Engineering
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Temporal databases naturally contain a wealth of information that can be unearthed by knowledge discovery and data mining techniques. Discovering association rules in market basket data have been widely studied and many algorithms have been developed. In this study, we examine discovery of association rules in temporal databases. We use the enumeration operation of the temporal relational algebra to prepare the data for discovery of association rules. To observe the changes in association rules and their statistics over the time, we can apply an incremental association rule mining technique to a series of datasets obtained over consecutive time intervals.