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

Piotr Kulczycki; Szymon Łukasik

International Journal of Applied Mathematics and Computer Science (2014)

- Volume: 24, Issue: 1, page 133-149
- ISSN: 1641-876X

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topPiotr Kulczycki, and Szymon Łukasik. "An algorithm for reducing the dimension and size of a sample for data exploration procedures." International Journal of Applied Mathematics and Computer Science 24.1 (2014): 133-149. <http://eudml.org/doc/271922>.

@article{PiotrKulczycki2014,

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).},

author = {Piotr Kulczycki, Szymon Łukasik},

journal = {International Journal of Applied Mathematics and Computer Science},

keywords = {dimension reduction; sample size reduction; linear transformation; simulated annealing; data mining},

language = {eng},

number = {1},

pages = {133-149},

title = {An algorithm for reducing the dimension and size of a sample for data exploration procedures},

url = {http://eudml.org/doc/271922},

volume = {24},

year = {2014},

}

TY - JOUR

AU - Piotr Kulczycki

AU - Szymon Łukasik

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

JO - International Journal of Applied Mathematics and Computer Science

PY - 2014

VL - 24

IS - 1

SP - 133

EP - 149

AB - 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).

LA - eng

KW - dimension reduction; sample size reduction; linear transformation; simulated annealing; data mining

UR - http://eudml.org/doc/271922

ER -

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