Scalable, Distributed and Dynamic Mining of Association Rules
We propose a novel pattern tree called Pattern Count tree (PC-tree) which is a complete and compact representation of the database. We show that construction of this tree and then generation of all large itemsets requires a single database scan where as the current algorithms need at least two database scans. The completeness property of the PC-tree with respect to the database makes it amenable for mining association rules in the context of changing data and knowledge, which we call dynamic mining. Algorithms based on PC-tree are scalable because PC-tree is compact. We propose a partitioned distributed architecture and an efficient distributed association rule mining algorithm based on the PC-tree structure.
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