Probabilistic Load Balancing Method for Parallel Mining of all Frequent Itemsets

R. Kessl and P. Tvrdík (Czech Republic)


Parallel algorithms and architectures, data mining, frequent itemset mining, load balancing


In this paper, we present a new method of static load bal ancing for parallel mining of all frequent itemsets on a distributed-memory (DM) parallel machine. The method partitions the space of all frequent itemsets into subspaces of approximately the same size. Hence, it allows to bal ance the computational load for an arbitrary frequent item set mining algorithm.

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