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Abstract
Association rules represent an important class of knowledge that can be discovered. As databases grow, the discovered association rules need to be verified (some new rules may be added to the knowledge base and some obsolete rules may be removed from the knowledge base). Current research efforts are focused on inventing efficient ways for updating the previously discovered rules. Their primary aim is to avoid or minimize number of original database scans. Also, they aim to benefit from the intermediate data constructed during the earlier mining (the frequent itemsets obtained from the original database) to get the new associations.
The main objective of this thesis is to update and maintain the discovered association rules for the incremental transaction and temporal database. Two algorithms for incremental updating of association rules are proposed.
Extensive experiments are conducted to evaluate the proposed algorithms. Many incremental mining algorithms have been examined to compare their performance against the two proposed algorithms. The experiments demonstrated that the two proposed algorithms outperform their counterparts.