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Lamine M. Aouad Nhien-An Le-Khac Tahar M. Kechadi 《Knowledge and Information Systems》2010,23(1):55-72
In this article, we focus on distributed Apriori-based frequent itemsets mining. We present a new distributed approach which
takes into account inherent characteristics of this algorithm. We study the distribution aspect of this algorithm and give
a comparison of the proposed approach with a classical Apriori-like distributed algorithm, using both analytical and experimental
studies. We find that under a wide range of conditions and datasets, the performance of a distributed Apriori-like algorithm
is not related to global strategies of pruning since the performance of the local Apriori generation is usually characterized
by relatively high success rates of candidate sets frequency at low levels which switch to very low rates at some stage, and
often drops to zero. This means that the intermediate communication steps and remote support counts computation and collection
in classical distributed schemes are computationally inefficient locally, and then constrains the global performance. Our
performance evaluation is done on a large cluster of workstations using the Condor system and its workflow manager DAGMan.
The results show that the presented approach greatly enhances the performance and achieves good scalability compared to a
typical distributed Apriori founded algorithm. 相似文献
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