COMPARATIVE STUDY OF DISTRIBUTED FREQUENT PATTERN MINING ALGORITHMS FOR BIG SALES DATA

Association rule mining plays an important role in decision support system. Nowadays in the era of internet, various online marketing sites and social networking sites are generating enormous amount of structural/semi structural data in the form of sales data, tweets, emails, web pages and so on. This online generated data is too large that it becomes very complex to process and analyze it using traditional systems which consumes more time. This paper overcomes the main memory bottleneck in single computing system. There are two major goals of this paper. In this paper, big sales dataset of AMUL dairy is preprocessed using Hadoop Map Reduce that convert it into the transactional dataset. Then, after removing the null transactions; distributed frequent pattern mining algorithm MR-DARM (Map Reduce based Distributed Association Rule Mining) is used to find most frequent item set. Finally, strong association rules are generated from frequent item sets. The paper also compares the time efficiency of MR-DARM algorithm with existing Count Distributed Algorithm (CDA) and Fast Distributed Mining (FDM) distributed frequent pattern mining algorithms. The compared algorithms are presented together with experimental results that lead to the final conclusions.