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. © 2006 Springer.
CITATION STYLE
De Falco, I., Tarantino, E., Cioppa, A. D., & Fontanella, F. (2006). An innovative approach to genetic programming-based clustering. Advances in Soft Computing, 34, 55–64. https://doi.org/10.1007/3-540-31662-0_4
Mendeley helps you to discover research relevant for your work.