Most of the classical clustering algorithms are strongly dependent on, and sensitive to, parameters such as number of expected
clusters and resolution level. To overcome this drawback, a Genetic Programming framework, capable of performing an automatic
data clustering, is presented. Moreover, a novel way of representing clusters which provides intelligible information on patterns
is introduced together with an innovative clustering process. The effectiveness of the implemented partitioning system is
estimated on a medical domain by means of evaluation indices.