Comparative Analysis of MapReduce Framework for Efficient Frequent Itemset Mining in Social Network Data

Abstract

Social networking sites are the virtual community for sharing information among the people It raises its popularity tremendously over the past few years Many social networking sites like Twitter Facebook WhatsApp Instragram LinkedIn generates tremendous amount data Mining such huge amount of data can be very useful Frequent itemset mining plays a significant role to extract knowledge from the dataset Traditional frequent itemsets method is ineffective to process this exponential growth of data almost terabytes on a single computer Map Reduce framework is a programming model that has emerged for mining such huge amount of data in parallel fashion In this paper we have discussed how different MapReduce techniques can be used for mining frequent itemsets and compared each other s to infer greater scalability and speed in order to find out the meaningful information from large datasets