A Novel Mapreduce Lift Association Rule Mining Algorithm (MRLAR) for Big Data
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2016, Vol 7, Issue 3
Abstract
Big Data mining is an analytic process used to discover the hidden knowledge and patterns from a massive, complex, and multi-dimensional dataset. Single-processor's memory and CPU resources are very limited, which makes the algorithm performance ineffective. Recently, there has been renewed interest in using association rule mining (ARM) in Big Data to uncover relationships between what seems to be unrelated. However, the traditional discovery ARM techniques are unable to handle this huge amount of data. Therefore, there is a vital need to scalable and parallel strategies for ARM based on Big Data approaches. This paper develops a novel MapReduce framework for an association rule algorithm based on Lift interestingness measurement (MRLAR) which can handle massive datasets with a large number of nodes. The experimental result shows the effi-ciency of the proposed algorithm to measure the correlations between itemsets through integrating the uses of MapReduce and LIM instead of depending on confidence.
Authors and Affiliations
Nour Oweis, Mohamed Fouad, Sami Oweis, Suhail Owais, Vaclav Snasel
Keywords
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- EP ID EP164448
- DOI 10.14569/IJACSA.2016.070321
- Views 91
- Downloads 0
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