Efficient mining of frequent itemsets in social network data based on MapReduce framework
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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In this paper we attempt to maximize the efficiency of the parallel Apriori Algorithm. The paper analyzes the performance of the algorithm over different datasets and over n processors on a commodity cluster of machines. In the Apriori Algorithm all processes need to synchronize after every pass. If any process is assigned more load than other processes in the system, the slowest process will dictate the speed of the program. It is therefore important to ensure that load is equally balanced among all processes. Our algorithm determines the no. of running processes and divides the load equally so as to maximize the system performance and its efficiency. The experiments conducted show that the parallel algorithm scales well to the number of processes and also improves on the efficiency by effective load balancing.