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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Genetic Algorithm for Multidimensional Scaling over Mixed and Incomplete Data
verfasst von : P. Tecuanhuehue-Vera, Jesús Ariel Carrasco-Ochoa, José Fco. Martínez-Trinidad
Erschienen in: Pattern Recognition
Verlag: Springer Berlin Heidelberg
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Multidimensional scaling maps a set of
n
-dimensional objects into a lower-dimension space, usually the Euclidean plane, preserving the distances among objects in the original space. Most algorithms for multidimensional scaling have been designed to work on numerical data, but in soft sciences, it is common that objects are described using quantitative and qualitative attributes, even with some missing values. For this reason, in this paper we propose a genetic algorithm especially designed for multidimensional scaling over mixed and incomplete data. Some experiments using datasets from the UCI repository, and a comparison against a common algorithm for multidimensional scaling, shows the behavior of our proposal.