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PCA Algorithms in the Visualization of Big Data from Polish Digital Libraries

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Multimedia and Network Information Systems (MISSI 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 833))

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Abstract

The visualization of large data sets from Polish digital libraries requires proper preparation of a comprehensive consolidated data set. Differences in the organizational systems of digital resources, and other factors affecting the heterogeneity of distributed data and metadata, require the use of clustering algorithms. To achieve this goal, the authors decided to use the PCA method and compare it with k-means results. PCA fulfills the condition of efficient size reduction for multidimensional data but is largely sensitive to deviations and differences in stochastic distributions. To eliminate the problem of noise in the input data, the deterministic model in the form of the Langevin function was used first. This leads to the “flattening” of the distribution of factors influencing the data structure. Due to such an approach, the most relevant categories to information systems were distinguished and Polish digital libraries were visualized.

This research is sponsored by National Science Center (NCN) under grant: 2013/11/B/HS2/03048/Digital knowledge structure and dynamics analysis by means of visualisation

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Author information

Authors and Affiliations

  1. College of School and Media Culture, Torun, Poland

    Grzegorz Osinski

  2. Nicolaus Copernicus University, Torun, Poland

    Veslava Osinska

  3. Wroclaw University, Wroclaw, Poland

    Piotr Malak

Authors
  1. Grzegorz Osinski
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  2. Veslava Osinska
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  3. Piotr Malak
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Corresponding author

Correspondence to Grzegorz Osinski .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Kazimierz Choroś

  2. Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Marek Kopel

  3. Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Elżbieta Kukla

  4. Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Andrzej Siemiński

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Osinski, G., Osinska, V., Malak, P. (2019). PCA Algorithms in the Visualization of Big Data from Polish Digital Libraries. In: Choroś, K., Kopel, M., Kukla, E., Siemiński, A. (eds) Multimedia and Network Information Systems. MISSI 2018. Advances in Intelligent Systems and Computing, vol 833. Springer, Cham. https://doi.org/10.1007/978-3-319-98678-4_52

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