Abstract
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.
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De Falco, I., Tarantino, E., Cioppa, A., Fontanella, F. (2006). An Innovative Approach to Genetic Programming—based Clustering. In: Abraham, A., de Baets, B., Köppen, M., Nickolay, B. (eds) Applied Soft Computing Technologies: The Challenge of Complexity. Advances in Soft Computing, vol 34. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31662-0_4
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DOI: https://doi.org/10.1007/3-540-31662-0_4
Publisher Name: Springer, Berlin, Heidelberg
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