Dimensionality Reduction Methods: The Comparison Of Speed And Accuracy

Jelena Zubova, Olga Kurasova, Marius Liutvinavičius


This research focuses on big data visualization that is based on dimensionality reduction methods. We propose a multi-level method for data clustering and visualization. Whole data mining process is divided into separate steps. For each step particular dimensionality reduction and visualization method is applied considering to data volume and type.  The selection of methods is based on their speed and accuracy. Therefore the comparison of the selected methods is made according to these two criteria. Three groups of datasets containing different kind of data are used for methods evaluation.  The factors that influence speed or accuracy are determined. The rank of investigated methods based on research results is presented in this paper.

DOI: http://dx.doi.org/10.5755/j01.itc.47.1.18813


big data; dimensionality reduction; data visualization

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Print ISSN: 1392-124X 
Online ISSN: 2335-884X