Microarray gene expression data association rules mining based on JG-Tree

The main techniques currently employed in analyzing microarray expression data are clustering and classification. In this paper we propose to use association rules to mine the association relationships among different genes under the same experimental condition. These kinds of relations may also exist across many different experiments with various experimental conditions. In this paper, a new approach, called LIS-growth (Large ItemSet growth) tree, is proposed for mining the microarray data. Our approach uses a new data structure, JG-tree (Jiang, Gruenwald), and a new data partition format for gene expression level data. Each data value can be presented by a sign bit, fractions and exponent bits. Each bit at the same position can be organized into a JG-tree. A JG-tree is a lossless and compression tree. It can be built on fly, a kind of real-time compression for bits string. 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 real-life datasets from the gene expression database at Stanford University.