Association Rule Mining in Big Data using MapReduce approach in Hadoop
Journal Title: GRD Journal for Engineering - Year 2016, Vol 1, Issue 0
Abstract
The concept of Association rule mining is an important task in data mining. In case of big data the large volume of data makes is impossible to generate rules at a faster pace. By making use of parallel execution in Hadoop using the MapReduce framework, the rules can be generated much faster and in an efficient way. The existing method transforms the input dataset into binomial representation before processing them using MapReduce. But binomial conversion is not user-friendly since it is complex in case of continuous values. In this paper, an improved and scalable algorithm is proposed for association rule mining that will convert the input dataset into key-value pairs instead of binomial. All the stages of proposed association rule mining algorithm are parallelized using MapReduce. The proposed algorithm works on high cardinality features and so no dimension detection is needed.
Authors and Affiliations
J. Jenifer Nancy, M. Jansi Rani, Dr. D. Devaraj
Keywords
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