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Objectives: To achieve improved performance of FP-Growth based Association Rule Mining algorithm for massive data by effective utilization of storage, execution capability and improved partition technique within the Hadoop MapReduce framework. Methodology: The proposed methodology has four main phases: In the first phase, the item sets for finding the frequent pattern are encoded and thus minimizes the expensive operation for large data set. In the second phase, improved hash partitioning reduces the network overhead and improves the communication speed within the MapReduce phase for each item set. The effective usage of network bandwidth and storage is obtained by the impact of compression in the third phase. The use of combiner in final phase for frequent item setminingminimizes the overhead of reduce phase by finding the pattern in each partition andminimizes the overall execution timeof the FP-Growth algorithm. Findings: FP-Growth based association rule mining algorithm is designed for parallel execution on distributed cluster of servers. Changes to the MapReduce implementation of FP-Growth with the impact of encoding. Improved hash partitioning, compression and configuration results in a significant performance gain with better improvement in execution time. Novelty/Improvements: According to the experimental results, the changes in storage and processing level within theMapReduce framework improves the overall performance of the parallel frequent item setmining in Hadoop cluster.

An efficient FP-Growth based association rule mining algorithm using Hadoop MapReduce