<abstract xmlns="http://www.w3.org/1999/xhtml"><p> The paper deals with the issue of reducing the dimension and size of a data set (random sample) for exploratory data analysis procedures. The concept of the algorithm investigated here is based on linear transformation to a space of a smaller dimension, while retaining as much as possible the same distances between particular elements. Elements of the transformation matrix are computed using the metaheuristics of parallel fast simulated annealing. Moreover, elimination of or a decrease in importance is performed on those data set elements which have undergone a significant change in location in relation to the others. The presented method can have universal application in a wide range of data exploration problems, offering flexible customization, possibility of use in a dynamic data environment, and comparable or better performance with regards to the principal component analysis. Its positive features were verified in detail for the domain’s fundamental tasks of clustering, classification and detection of atypical elements (outliers).</p></abstract>

The paper deals with the issue of reducing the dimension and size of a data set (random sample) for exploratory data analysis procedures. The concept of the algorithm investigated here is based on linear transformation to a space of a smaller dimension, while retaining as much as possible the same distances between particular elements. Elements of the transformation matrix are computed using the metaheuristics of parallel fast simulated annealing. Moreover, elimination of or a decrease in importance is performed on those data set elements which have undergone a significant change in location in relation to the others. The presented method can have universal application in a wide range of data exploration problems, offering flexible customization, possibility of use in a dynamic data environment, and comparable or better performance with regards to the principal component analysis. Its positive features were verified in detail for the domain’s fundamental tasks of clustering, classification and detection of atypical elements (outliers).

An algorithm for reducing the dimension and size of a sample for data exploration procedures

Systems Research Institute Polish Academy of Sciences, ul. Newelska 6, 01-447 Warsaw, Poland

Department of Automatic Control and Information Technology Cracow University of Technology, ul. Warszawska 24, 31-155 Cracow, Poland

International Journal of Applied Mathematics and Computer Science

The paper deals with the issue of reducing the dimension and size of a data set (random sample) for exploratory data analysis procedures. The concept of the...

An algorithm for reducing the dimension and size of a sample for data exploration procedures

Online veröffentlicht: 25. März 2014

Seitenbereich: 133 - 149

DOI: https://doi.org/10.2478/amcs-2014-0011

Schlüsselwörter
dimension reduction, sample size reduction, linear transformation, simulated annealing, data mining.

This content is open access.

An algorithm for reducing the dimension and size of a sample for data exploration procedures

Online veröffentlicht: 25. März 2014

Seitenbereich: 133 - 149

DOI: https://doi.org/10.2478/amcs-2014-0011

Schlüsselwörterdimension reduction, sample size reduction, linear transformation, simulated annealing, data mining.

This content is open access.

Schlüsselwörter
dimension reduction, sample size reduction, linear transformation, simulated annealing, data mining.