A new variable---length genome genetic algorithm for data clustering in semeiotics

Authors:
I. De Falco;E. Tarantino;A. Delia Cioppa;F. Fontanella
Affiliations:
ICAR - CNR, Naples, Italy;ICAR - CNR, Naples, Italy;University of Salerno, Fisciano (SA), Italy;University of Naples, Italy
Venue:
Proceedings of the 2005 ACM symposium on Applied computing
Year:
2005

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Abstract

This paper focuses on the introduction of a new evolutionary algorithm for data clustering, the Self-sizing Genome Genetic Algorithm. It is akin to a messy Genetic Algorithm and does not use a priori information about the number of clusters. A new recombination operator, gene-pooling, is introduced, while 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 found clusters in unambiguously addressing towards pathologies. Comparison with other clustering tools is performed.