High Performance Computation of Big Data: Performance Optimization Approach towards a Parallel Frequent Item Set Mining Algorithm for Transaction Data based on Hadoop MapReduce Framework

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Guru Prasad M S 1,* Nagesh H R 2 Swathi Prabhu 3

1. SDMIT/CSE, Ujire, 577240, India

2. MITE/CSE, Moodbidri, 574227, India

3. SMVITM/CSE, Udupi, 576115, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2017.01.08

Received: 6 Feb. 2016 / Revised: 1 Jun. 2016 / Accepted: 15 Aug. 2016 / Published: 8 Jan. 2017

Index Terms

Big Data, Hadoop, MapReduce, Hadoop Distributed File System (HDFS), Apriori MapReduce, FP-growth MapReduce


The Huge amount of Big Data is constantly arriving with the rapid development of business organizations and they are interested in extracting knowledgeable information from collected data. Frequent item mining of Big Data helps with business decision and to provide high quality service. The result of traditional frequent item set mining algorithm on Big Data is not an effective way which leads to high computation time. An Apache Hadoop MapReduce is the most popular data intensive distributed computing framework for large scale data applications such as data mining. In this paper, the author identifies the factors affecting on the performance of frequent item mining algorithm based on Hadoop MapReduce technology and proposed an approach for optimizing the performance of large scale frequent item set mining. The Experiments result shows the potential of the proposed approach. Performance is significantly optimized for large scale data mining in MapReduce technique. The author believes that it has a valuable contribution in the high performance computing of Big Data.

Cite This Paper

Guru Prasad M S, Nagesh H R, Swathi Prabhu,"High Performance Computation of Big Data: Performance Optimization Approach towards a Parallel Frequent Item Set Mining Algorithm for Transaction Data based on Hadoop MapReduce Framework", International Journal of Intelligent Systems and Applications(IJISA), Vol.9, No.1, pp.75-84, 2017. DOI:10.5815/ijisa.2017.01.08


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