Microarray gene expression data association rules mining based on BSC-tree and FIS-tree

In this paper we propose to use association rules to mine the association relationships among different genes under the same experimental conditions. These kinds of relations may also exist across many different experiments with various experimental conditions. In this paper, a new approach, called FIS-tree mining, is proposed for mining the microarray data. Our approach uses two new data structures, BSC-tree and FIS-tree, and a data partition format for gene expression level data. Based on these two new data structures it is possible to mine the association rules efficiently and quickly from the gene expression database. Our algorithm was tested using the two real-life gene expression databases available at Stanford University and Harvard Medical School and was shown to perform better than the two existing algorithms, Apriori and FP-Growth.