Data mining has been recognized as a new area for database research. In the area of knowledge discovery, association rule mining focuses on finding a set of all subsets of items, that frequently occur in database records and then extracting the interesting patterns found among them. Recent advancement in biotechnology has produced a massive amount of raw biological data which are accumulating at an exponential rate. Tuberculosis remains one of the leading causes of morbidity and mortality of mankind throughout the world. Mammalian cell entry (mce) gene is crucial in conferring virulence to Mycobacterium tuberculosis. This paper focuses on finding association between Mycobacterium tuberculosis and Mycobacterium leprae based on presence of mammalian cell entry gene. This gives useful insight for microbiologists in identifying and clustering the organisms.