Association rule mining in the distributed database has become an important area of research, where frequent pattern or itemsets are found in a large distributed data of varied sizes stored at multiple sites. In recent years, new efficient data structures have been proposed for the data mining. A new algorithm named as QDFIN(Quick distributed frequent itemset mining using nodeset) is proposed in this paper which uses the efficient nodeset data structureto store the candidate itemsets locally at

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... each site and zero-first technique to balance the load and pruning to reduce the candidate sets. The algorithm is implemented and the speed performance is compared with PFIN and FDM using FP-Growth algorithms. Results shows that the proposed algorithm not only outperform other algorithms on varying size data partition but also on uniform distributed data on 4, 5 and 6 node setups.