online read us now
Paper details
Number 1 - March 2014
Volume 24 - 2014
An algorithm for reducing the dimension and size of a sample for data exploration procedures
Piotr Kulczycki, Szymon Łukasik
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).
Keywords
dimension reduction, sample size reduction, linear transformation, simulated annealing, data mining