Abstract
Association rules can reveal biological relevant relationship between genes and environments/categories. However, most existing association rule mining algorithms are rendered impractical on gene expression data, which typically contains thousands or tens of thousands of columns, but only tens of rows. The main problem is that these algorithms have an exponential dependence on the number of columns. Another shortcoming evident is that too many associations are generated from such kind of data. This chapter introduces a novel depth-first row-wise algorithm FARMER that is specially designed to efficiently discover and cluster association rules into interesting rule groups (IRGs) that satisfy user-specified minimum support, confidence, and chi-square value thresholds on biological datasets as opposed to finding association rules individually. Based on FARMER, the chapter also introduces a prototype system that integrates semantic mining and visual analysis of IRGs mined from gene expression data.
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