Abstract
Medical semeiotics often deals with patient databases and would greatly benefit from efficient clustering techniques. In this paper a new evolutionary algorithm for data clustering, the Self–sizing Genome Genetic Algorithm, is introduced. It does not use a priori information about the number of clusters. Recombination takes place through a brand–new operator, i.e., gene–pooling, and fitness is based on simultaneously maximizing intra–cluster homogeneity and inter–cluster separability. This algorithm is applied to clustering in dermatological semeiotics. Moreover, a Pathology Addressing Index is defined to quantify utility of the clusters making up a proposed solution in unambiguously addressing towards pathologies.
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De Falco, I., Tarantino, E., Cioppa, A.D., Gagliardi, F. (2006). A Genetic Algorithm with Self–sizing Genomes for Data Clustering in Dermatological Semeiotics. 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_34
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DOI: https://doi.org/10.1007/3-540-31662-0_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31649-7
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