Reduction of dimension and size of data set by parallel fast simulated annealing
Autor
Kulczycki, Piotr
Łukasik, Szymon
Data wydania
2014
Miejsce wydania
Cham, [etc.]
Wydawca
Springer
Opublikowane w
Issues and Challenges of Intelligent Systems and Computational Intelligence / red. L.T. Kóczy, C. Pozna, J. Kacprzyk
Strony
273-290
Język
angielski
ISBN
978-3-319-03205-4
978-3-319-03206-1 (on-line)
DOI
10.1007/978-3-319-03206-1_19
Abstrakt
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.
