Creator:
Kulczycki, Piotr ; Łukasik, Szymon
Contributor:
Kowal, Marek - red. ; Korbicz, Józef - red.
Title:
An algorithm for reducing the dimension and size of a sample for data exploration procedures
Subtitle:
Group publication title:
Subject and Keywords:
dimension reduction ; sample size reduction ; linear transformation ; simulated annealing ; data mining
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).
Publisher:
Zielona Góra: Uniwersytet Zielonogórski
Date:
Resource Type:
DOI:
Pages:
Source:
AMCS, volume 24, number 1 (2014) ; click here to follow the link