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Intelligent Systems for Computer Modelling pp 121–132Cite as

Cluster Analysis of Data with Reduced Dimensionality: An Empirical Study

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 423))

Abstract

Cluster analysis is an important high-level data mining procedure that can be used to identify meaningful groups of objects within large data sets. Various dimension reduction methods are used to reduce the complexity of data before further processing. The lower-dimensional projections of original data sets can be seen as simplified models of the original data. In this paper, several clustering algorithms are used to process low-dimensional projections of complex data sets and compared with each other. The properties and quality of clustering obtained by each method is evaluated and their suitability to process reduced data sets is assessed.

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Notes

  1. 1.

    http://www.cc.gatech.edu/projects/large_models/.

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Acknowledgements

This work was supported by the IT4Innovations Centre of Excellence project (CZ.1.05/1.1.00/02.0070), funded by the European Regional Development Fund and the national budget of the Czech Republic via the Research and Development for Innovations Operational Programme and by Project SP2015/146 of the Student Grant System, VŠB—Technical University of Ostrava.

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Authors and Affiliations

  1. IT4Innovations & Department of Computer Science, VŠB Technical University of Ostrava, Ostrava, Czech Republic

    Pavel Krömer & Jan Platoš

Authors
  1. Pavel Krömer
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  2. Jan Platoš
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Corresponding author

Correspondence to Pavel Krömer .

Editor information

Editors and Affiliations

  1. Faculty of Electrical Eng. and Comp., VŠB-Technical University of Ostrava, Ostrava, Czech Republic

    Vítězslav Stýskala

  2. Department of Electrical Engineering, VŠB – Technical University of Ostrava, Ostrava, Czech Republic

    Dmitrii Kolosov

  3. Faculty of Electrical Engineering and, Technical University of Ostrava, Ostrava-Poruba, Czech Republic

    Václav Snášel

  4. after Jusup Balasagyn, Kyrgyz National University named, Bishkek, Kyrgyzstan

    Taalaybek Karakeyev

  5. Sci N/w for Innova and Research Exc, Machine Intelligence Research Labs, Washington, USA

    Ajith Abraham

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Krömer, P., Platoš, J. (2016). Cluster Analysis of Data with Reduced Dimensionality: An Empirical Study. In: Stýskala, V., Kolosov, D., Snášel, V., Karakeyev, T., Abraham, A. (eds) Intelligent Systems for Computer Modelling . Advances in Intelligent Systems and Computing, vol 423. Springer, Cham. https://doi.org/10.1007/978-3-319-27644-1_12

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  • DOI: https://doi.org/10.1007/978-3-319-27644-1_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27642-7

  • Online ISBN: 978-3-319-27644-1

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