Association Rule Mining on Big Data – A Survey

Association Rule Mining on Big Data – A Survey
Authors : K. Geethanandhini , R. Nedunchezhian
Publication Date: 04-05-2016


Author(s):  K. Geethanandhini , R. Nedunchezhian

Published in:   International Journal of Engineering Research & Technology

License:  This work is licensed under a Creative Commons Attribution 4.0 International License.

Website: www.ijert.org

Volume/Issue:   Volume. 5 - Issue. 05 , May - 2016

e-ISSN:   2278-0181

 DOI:  http://dx.doi.org/10.17577/IJERTV5IS050049


Frequent pattern mining is the key concept in Association Rule Mining task. Main aim of frequent pattern mining is to find the recurrent patterns occurring in a dataset. Finding patterns identify the relationship between items in an item domain, these relationships are useful for strategic decision making. Data is flooded in a day to day life, called “Big Data”, because massive amount of data is produced everywhere. Mining frequent patterns from the huge volumes of data has many challenges due to memory requirement, multiple data dimensions, heterogeneity of data and so on. The complexities related to mining frequent itemsets from a Big Data can be minimized by parallelizing the mining task with Map Reduce framework in Hadoop Cluster [1]. In this paper, an introduction to Big Data, Association Rule Mining, concepts and basic methods for frequent pattern mining are given. The various methods proposed by different authors to mine frequent patterns from enormous dataset effectively are also discussed.


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