Performance study of distributed Apriori-like frequent itemsets mining
This work addresses performance issues in distributed data mining for researchers and practitioners, but it is incremental as it builds on existing Apriori-based algorithms.
The paper tackles the inefficiency of distributed Apriori-like frequent itemsets mining by proposing a new approach that addresses local computational bottlenecks, showing it greatly enhances performance and scalability compared to classical methods in experiments on a large cluster.
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.