Parallel Generalized Association Rule Mining on Large Scale PC Cluster

Abstract

One of the most important problems in data mining is discovery of association rules in large database. In our previous study, we proposed parallel algorithms and candidate duplication based load balancing strategies for mining generalized association rules and showed our algorithms could attain good performance on 16 nodes parallel computer system. However, as the number of nodes increase, it would be difficult to achieve flat workload distribution.

In this paper, we present the candidate partition based load balancing strategy for parallel algorithm of generalized association rule mining. This strategy partitions the candidate itemsets so that the number of candidate probes for each node is equalized each other with estimated support count by the information of previous pass. Moreover, we implement the parallel algorithms and load balancing strategies for mining generalized association rules on a cluster of 100 PCs interconnected with an ATM network, and analyze the performance using a large amount of transaction dataset. Through the several experiments, we showed the load balancing strategy, which partition the candidate itemsets with considering the distribution of candidate probes and duplicate the frequently occurring candidate itemsets, can attain high performance and achieve good workload distribution on one hundred PC cluster system.