Social Network is a network of social involvements and personal relationships. Social Networks involve information sharing between people at all times which results in producing large amount of data produced in this social network environment which can be extremely useful. As social networks are increased, its storage also increases. By observation, it has been discovered that most of social sites have redundant, noisy data. To get such optimized information, Social network analysis focuses on mining out the pattern of user's interaction. For such mining the paper proposes to implement Mining of association rules which helps in the discovery of associations, correlations, statistically relevant patterns, causality, emerging patterns, and other data mining tasks in social networks. Most of the traditional frequent item set mining algorithms is ineffective due to either enormous resource requirements or large communications overhead. Cloud computing has shown that processing very large datasets over clusters can be done by providing the right programming model. As a programming model working in parallel form, Map-Reduce, one of techniques for cloud computing, has emerged in the mining of datasets scaling from terabyte or larger on clusters of computers. The present paper focuses on making use of a proposed algorithm for association rule mining employing the MapReduce frame of reference which deals with Hadoop, a parallel store and computing platform. This will help to improve efficiency and accuracy of the given system.

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Association Rules Mapreduce