Comparative Analysis of Association Rule Mining Algorithms in Mining Frequent Patterns

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Published: Jun 22, 2017
DOI: https://doi.org/10.26483/ijarcs.v8i5.3880
Keywords:
Apriori, Association Rule Mining Algorithm, Data mining, FP-tree, Minimum support, Predictive Apriori, Predictive Accuracy, Pruning

Main Article Content

Sinthuja M
Annamalai University
Dr. Puviarasan N
Annamalai University
Dr. Aruna P
Annamalai University

Abstract

Finding frequent patterns in a huge transaction database has wide scope in research. Here in this paper we consider three of the algorithms in an association rule namely Apriori, Predictive Apriori and FP-Growth algorithm. Experiments are done to compare the result of these three algorithms with distinct datasets and present the result. Based on the results, we find that FP-Growth algorithm is more worthwhile than the Predictive Apriori and Apriori algorithms as time consumption is low and the need for candidate patterns is done away.

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Vol. 8 No. 5 (2017): May-June 2017
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Author Biographies

Sinthuja M, Annamalai University

Computer Science and Engineering

Dr. Puviarasan N, Annamalai University

Department of Computer and Information Science

Dr. Aruna P, Annamalai University

Department of Computer and Information Science

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