期刊名称：Journal of Advances in Information Technology
印刷版ISSN：1798-2340
出版年度：2012
卷号：3
期号：1
页码：69-76
DOI：10.4304/jait.3.1.69-76
语种：English
出版社：Academy Publisher
摘要：In proposed approach, we introduce the problem of mining association rules in large relational tables containing both quantitative and categorical attributes. We have proposed an algorithm for Discovery of Scalable Association Rules from large set of multidimensional quantitative datasets using k-means clustering method based on the range of the attributes in the rules and Equi-depth partitioning using scale k-means for obtaining better association rules with high support and confidence. The discretization process is used to create intervals of values for every one of the attributes in order to generate the association rules. The result of the proposed algorithm discover association rules with high confidence and support in representing relevant patterns between project attributes using the scalable k-means .The experimental studies of proposed algorithm have been done and obtain results are quite encouraging.
关键词：Data Mining; Association rules; k-means clustering; CBA tool; Discretization; Partitioning
