An Effective and Optimized Approach to Association Rule Mining using GPGPU
Keywords:
Associative rule mining, heterogenous parallel programming, CUDA, frequent pattern miningAbstract
Frequent Pattern Growth (FP-Growth) is a data mining technique, FP-growth algorithm introduced frequent pattern tree (FP-tree), stored as frequent item-sets in a compressed way. It overcomes drawback of candidate generation approach of multiple database scan but at the same time the transaction identifiers can be quite long taking substantial memory space and computation time. An optimised data structure viz. the Multi-Path Graph is used to improve the utilization and increase the efficiency of data mining techniques. Here we will be using graph as a data structure for storing frequent patterns in the memory. The graph structure will help to mine these frequent patterns without constructing FP-trees. However FP-Growth and MP-Graph fail to process extremely vast data-sets optimally. So we will be attempting to compare FP-Growth with MP-Graph as per its efficiency and memory utilization capability using parallelization techniques. We will try to achieve parallelization using CUDA, and bring forth a comparison of both the mining techniques.
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