A new methodology for computer aided diagnosis in digital mammography using unsupervised classification and classdependent feature selection is presented. This technique considers unlabeled data and provides unsupervised classes that give a better insight into classes and their interrelationships, thus improving the overall effectiveness of the diagnosis. This technique is also extended to utilize biclustering methods, which allow for definition of unsupervised clusters of both pathologies and features. This has potential to provide more flexibility, and hence better diagnostic accuracy, than the commonly used feature selection strategies. The developed methods are applied to diagnose digital mammographic images from the Mammographic Image Analysis Society (MIAS) database and the results confirm the potential for improving the current diagnostic rates.