@misc{Kulczycki_Piotr_An,
 author={Kulczycki, Piotr and Łukasik, Szymon},
 howpublished={online},
 publisher={Zielona Góra: Uniwersytet Zielonogórski},
 language={eng},
 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.},
 abstract={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.},
 abstract={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).},
 type={artykuł},
 title={An algorithm for reducing the dimension and size of a sample for data exploration procedures},
 keywords={dimension reduction, sample size reduction, linear transformation, simulated annealing, data mining},
}