MapReduce-Based Frequent Pattern Mining Framework with Multiple Item Support

Abstract

The analysis of big data mining for frequent patterns is become even more problematic. Many efficient itemset mining algorithms to set a multiple support values for each transaction which could seem feasible as real life applications. To solve problem of single support have been discovered in the past. Since, we know that parallel and distributed computing are valid approaches to deal with large datasets. In order to reduce the search space, we using MISFP-growth algorithm without the process of rebuilding and post pruning steps. Accordingly, in this paper we proposed a model to use of MapReduce framework for implement the parallelization under multi-sup values, thereby improving the overall performance of mining frequent patterns and rare items accurately and efficiently. © Springer International Publishing AG 2017.