文章基本信息

标题：Minimizing Spurious Patterns Using Association Rule Mining
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作者：Ruchi Goel ; Dr. Parul Agarwal
期刊名称：International Journal of Computer Trends and Technology
电子版ISSN：2231-2803
出版年度：2014
卷号：10
期号：4
页码：192-196
DOI：10.14445/22312803/IJCTT-V10P133
出版社：Seventh Sense Research Group
摘要：Most of the clustering algorithms extract patterns which are of least interest. Such pattern consists of data items which usually belong to widely different support levels. Such data items belonging to different support level have weak association between them, thus producing least interested patterns which are of least interest. The reason behind this problem is that such existing algorithms do not have the basic knowledge regarding the cooccurrence relationship between data items. Such algorithm cannot even consider the knowledge regarding the cooccurrence relationship among the data items in them as if it consider such knowledge, the goal of the algorithm will conflict with this knowledge. I am going to propose a solution to this problem by extracting highly correlated and interested patterns known as maximized patterns. Confidence measure will be used to extract maximized patterns. In this framework, the data mining operation is performed not directly on the data set but the data mining is performed on the highly correlated intensive patterns. Using this strategy the effect of cross support pattern is also minimized. A minimum threshold value is also being used to regulate the intensive patterns.
关键词：Asymmetric data set; Cooccurrence relation; Intensive patterns; Minimum threshold; Spurious patterns