Author : Sanchita Sonar 1
Date of Publication :27th November 2017
Abstract: Data mining deals with the extraction of hidden predictive information from large databases. Main task of data mining is to discover action rules to take profitable decisions from huge database. However, with the exposing recent growth of size of data it is challenging to find out patterns of dataset. We have used a scalable approach for discovering action rules. The main goal of this algorithm is to build a mechanism that enables au-tomatic parallelization and data distribution for parallel mining of frequent itemsets on large clusters. We are proposing an algorithm inspired from FiDoop which run in-house Hadoop cluster. We are distributing dataset into number of blocks as each block allotted to each node for mining purpose. In this algorithm, we have pro- posed two parts, first part finds the frequent itemset in the dataset while the second part optimizes the efficiency of output. We also have optimized the mapper which gives high performance.
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