Parallelized Comprising for Apriori Algorithm Using Mapreduce Framework

Abstract: An integrating classification and association rule mining can produce more efficient and accurate classifiers than traditional techniques. The recently introduces MapReduce based association rule mining for extracting strong rules from large datasets. This mining is used later to develop a new large scale classifier. Map Reduce simulator was developed to evaluate the scalability of proposed apriori algorithms on MaReduce. The developed associative rule mining inherits the MapReduce scalability to huge datasets and to thousands of processing nodes. For finding frequent item sets, it uses hybrid approach between miners that uses counting methods. The new miner generates same rules that usually generated using apriori algorithms. Map Reduce classifier that based MapReduce associative rule mining. For the purpose of data mining to big data, parallel comprising this algorithm employs different approaches in rule discovery, rule from frequent itemsets, and rule pruning methods in these research fields. The present Map Reduce was developed to measure the scalability of MapReduce based applications easily and quickly, in this paper comprehensive to evaluate an accurate and effective classification technique, highly competitive and scalable if compared with other traditional and associative classification approaches.

Keywords: Data mining, MapReduce, MRApriori algorithm, Association rule mining.