文章基本信息

标题：Quantitative Analysis of Apriori and Eclat Algorithm for Association Rule Mining
本地全文：下载
作者：Tanu Jain ; Dr. A.K Dua Varun Sharma
期刊名称：International Journal of Engineering and Computer Science
印刷版ISSN：2319-7242
出版年度：2015
卷号：4
期号：10
页码：14649-14652
DOI：10.18535/ijecs/v4i10.18
出版社：IJECS
摘要：Apriori and Eclat algorithms are the mostly used algorithms in the area of association rules mining. They aregenerally used for mining of frequent item sets and to discover associations between these frequent item sets. R is a domainspecific language for data analysis and analytics. It is already being used across different disciplines from Computer Scienceto Social Sciences. In this research a qualitative and quantitative analysis of Apriori and Eclat algorithms is done using REnvironment. Different R-Packages and libraries are used for the access of different datasets and their connectivity with R. Inthis research, both algorithms have been implemented using different data sets and are further analysed on the basis of theirperformance. The performance analysis is based on total execution time taken by these algorithms in order to identify theirquantitative performance and speedup with different volume of datasets.
关键词：Apriori; Eclat; Association Rule Mining; R language; R Environment