Optimization of Association Rule Mining using FP_Growth Algorithm with GA
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2017, Vol 5, Issue 4
Abstract
Frequent pattern mining is one of the active research themes in data mining which covers a broad spectrum of data mining tasks viz. Association rules, correlations, causality, ratio rules, emerging patterns etc. In this paper, we expand the horizon of frequent pattern mining by introducing an efficient algorithm for mining multi-level and multi-dimensional frequent patterns with flexible support constraints. We analyze the scalability of our algorithm and study its performance on different data sets. We have tested our two hybrid algorithms viz. Apriori+fpga and firefly+fpga with traditional apriori and ga. The objective of this paper is to compare the performance of the genetic algorithm for association rule mining by combining it with other algorithms. The algorithms when tested on abalone dataset that indicates that the accuracy depends mainly on the fitness function which is the key parameter. The crossover probability brings changes in convergence rate with minimal changes in accuracy. The size of the dataset and relationship between its attributes also plays a role in achieving the optimum accuracy. Theoretical analysis and experimental results show that the performance of firefly+fpga is better than other algorithms, however the performance of apriori+fpga was found better than traditional apriori and ga.
Authors and Affiliations
Abhishek Kumar Singh, Deepak Sinwar
Keywords
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- EP ID EP23861
- DOI http://doi.org/10.22214/ijraset.2017.4155
- Views 284
- Downloads 9
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