Kernel based gene expression pattern discovery and its application on cancer classification

Association rules have been widely used in gene expression data analysis. However, there is no systematical way to select interesting rules from the millions of rules generated from high dimensional gene expression data. In this study, a kernel density estimation based measurement is proposed to evaluate the interestingness of the association rules. Several pruning strategies are also devised to efficiently discover the approximate top-k interesting patterns. Finally, over-fitting problem of the classification model is addressed by using conditional independence test to eliminate redundant rules. Experimental results show the effectiveness of the proposed interestingness measure and classification model.