抄録
Microarray data contain the expression levels of thousands of genes in multiple samples. Biologists try to find interesting patterns in gene expressions which show significant characteristics such as meaningful contrast in compared groups. It is one of important issues to identify the contrasting gene or sample clusters with respect to the compared group. This kind of analysis provides information about the biological markers in disease development, diagnosis, recurrence, or progression. Due to the high dimensionality of microarray data, it is not easy to find out which gene or sample clusters show contrasting behavior. This paper proposes a method to mine all contrasting clusters in microarray data. It makes use of fuzzy techniques to determine contrast of expression data, and incorporates an association rule mining technique which takes into account the curse of dimensionality.
