A convenient application domain in biology is gene expression data, which include a large number of attributes (genes), and the associations among different genes are often particularly important. Although the expression values of genes are not binary, an appropriate discretization algorithm can convert these values to a suitable format for applying an association rule (AR) mining algorithm successfully. Even though gene expression data are quite convenient for association analysis, AR mining is also applied to other kinds of biological data. These applications are presented in this chapter, followed by a definition of.the problem of mining ARs. The chapter describes some important mining algorithms, preprocessing and postprocessing methods. Gene expression data, biological sequences, biological structural data, protein interaction networks, and biological texts are the most popular kinds of biological data that have been effectively analyzed using these data‐mining tool.