Minimizing Spurious Patterns Using Association Rule Mining
Journal Title: INTERNATIONAL JOURNAL OF COMPUTER TRENDS & TECHNOLOGY - Year 2014, Vol 10, Issue 4
Abstract
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 co-occurrence relationship between data items. Such algorithm cannot even consider the knowledge regarding the co-occurrence 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.
Authors and Affiliations
Ruchi Goel , Dr. Parul Agarwal
Keywords
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