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Clinically, cancer is a complex family of diseases. From the viewpoint of molecular biology, cancer is a genetic disease resulting from abnormal gene expression due to DNA instability, such as translocation, amplification, deletion or point mutations. The purpose was first to perform functional analysis of a set of cancerous genes, and to discover or predict the other human genes that are hypothetically cancer-related. To achieve this goal, an approach consisting of three major components was proposed. Firstly, an automatic system has been developed to collect different data sets from the Internet, like human genes, proteins, and functional domains. Secondly, the functional domain compositions extracted from proteins were adopted for grouping the cancerous genes into a number of clusters by hierarchical clustering and association rule methods. The functional domains commonly existing in a cluster of genes represented the characteristic of that cluster. A second set of hypothetically cancerous genes predicted by single nucleotide polymorphism (SNP) was utilized for testing purpose. The experimental result indicated that a total accuracy of 81.45% was reached. It is anticipates that the proposed approach can be applied to other genetic diseases with minor modification.