Reduction of dimension and size of data set by parallel fast simulated annealing
Author
Kulczycki, Piotr
Łukasik, Szymon
Release date
2014
Place of publication
Cham, [etc.]
Publisher
Springer
Published in
Issues and Challenges of Intelligent Systems and Computational Intelligence / red. L.T. Kóczy, C. Pozna, J. Kacprzyk
Pages
273-290
Language
English
ISBN
978-3-319-03205-4
978-3-319-03206-1 (on-line)
DOI
10.1007/978-3-319-03206-1_19
Abstract
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.
