Performance study of distributed Apriori-like frequent itemsets mining

TL;DR

This paper introduces a new distributed Apriori-like algorithm for frequent itemsets mining, demonstrating improved performance and scalability through analytical and experimental evaluation on a large cluster.

Contribution

A novel distributed Apriori approach that considers algorithm characteristics, leading to significant performance improvements over classical methods.

Findings

Performance of distributed Apriori is not mainly affected by pruning strategies.

Local candidate generation success rates vary, impacting global communication costs.

The proposed method achieves better scalability on large clusters.

Abstract

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.