CNAR-M: A model for mining critical negative association rules

Abstract

Association rules mining has been extensively studied in various multidiscipline applications. One of the important categories in association rule is known as Negative Association Rule (NAR). Significant NAR is very useful in certain domain applications; however it is hardly to be captured and discriminated. Therefore, in this paper we proposed a model called Critical Negative Association Rule Model (CNAR-M) to extract the Critical Negative Association Rule (CNAR) with higher Critical Relative Support (CRS) values. The result shows that the CNAR-M can mine CNAR from the benchmarked and real datasets. Moreover, it also can discriminate the CNAR with others association rules. © 2012 Springer-Verlag.