Reduction of dimension and size of data set by parallel fast simulated annealing / Piotr Kulczycki, Szymon Łukasik // W: Issues and challenges of intelligent systems and computational intelligence / eds. ... więcej

Reduction of dimension and size of data set by parallel fast simulated annealing / Piotr Kulczycki, Szymon Łukasik // W: Issues and challenges of intelligent systems and computational intelligence / eds. László T. Kóczy, Claudiu R. Pozna, Janusz Kacprzyk. - Cham [etc.] : Springer, 2014. - (Studies in Computational Intelligence, ISSN 1860-949X ; 530). - S. 273-290 : il. - Bibliogr. 41 poz., Abstr. - doi: 10.1007/978-3-319-03206-1_19. - ISBN 978-3-319-03205-4

A universal method of dimension and sample size reduction, designed for exploratory data analysis procedures, constitutes the subject of this paper. The dimension is reduced by applying linear transformation, ... więcej

A universal method of dimension and sample size reduction, designed for exploratory data analysis procedures, constitutes the subject of this paper. The dimension is reduced by applying linear transformation, with the requirement that it has the least possible influence on the respective locations of sample elements. For this purpose an original version of the heuristic Parallel Fast Simulated Annealing method was used. In addition, those elements which change the location significantly as a result of the transformation, may be eliminated or assigned smaller weights for further analysis. As well as reducing the sample size, this also improves the quality of the applied methodology of knowledge extraction. Experimental research confirmed the usefulness of the procedure worked out in a broad range of problems of exploratory data analysis such as clustering, classification, identification of outliers and others.