Efficient Large Scale Frequent Itemset Mining with Hybrid Partitioning Approach

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In today’s world, voluminous data are available which are generated from various sources in various forms. Mining or analyzing this large scale data in an efficient way so as to make them useful for the mankind is difficult with the existing approaches. Frequent itemset mining is one such technique used for analyzing in many fields like finance, health care system where the main focus is gathering frequent patterns and grouping them to be meaningful inorder to gather useful insights from the data. Some major applications include customer segmentation in marketing, shopping cart analyses, management relationship, web usage mining, player tracking and so on. Many parallel algorithms, like Dist-Eclat Algorithm, Big FIM algorithm are available to perform large scale Frequent itemset mining. In Dist-Eclat algorithm, datasets are partitioned using Round Robin technique which uses a hybrid partitioning approach, which can improve the overall efficiency of the system. The system works as follows: Initially the data collected are distributed by mapreduce. Then the local frequent k-itmesets are computed using FP-Tree and sent to the map phase. Later the mining results are combined to the center node. Finally, global frequent itemsets are gathered by mapreduce. The proposed system is expected to improve in efficiency by using hybrid partitioning approach in the datasets based on the identification of frequent items.