A novel grammar-based genetic programming approach to clustering
Ivanoe De Falco, Ernesto Tarantino, Antonio Della Cioppa, Francesco Fontanella · 2005
Most of the classical methods for clustering analysis require the user setting of number of clusters. To surmount this problem, in this paper a grammar-based Genetic Programming approach to automatic data clustering is presented. An innovative clustering process is conceived strictly linked to a novel cluster representation which provides intelligible information on patterns. The efficacy of the implemented partitioning system is estimated on a medical domain by exploiting expressly defined evaluation indices. Furthermore, a comparison with other clustering tools is performed.