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An Adaptive Neighborhood Retrieval Visualizer

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Hybrid Artificial Intelligent Systems (HAIS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12344))

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Abstract

We propose a novel adaptive version of the Neighborhood Retrieval Visualizer (NeRV). The data samples’ neighborhood widths are determined on the basis of the data scattering in the high-dimensional input space. The scattering of input data is measured using the inner-cluster variance quantity, obtained as a result of the preliminary data clustering in the input space. The combination of the pre-clustering and the subsequent NeRV projection can be recognized as a hybrid approach. The experimental study carried out on two different real datasets verified and confirmed the effectiveness of the introduced approach and the correctness of the theoretical claim of the paper.

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Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering, Warsaw University of Technology, Warsaw, Poland

    Dominik Olszewski

Authors
  1. Dominik Olszewski
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Correspondence to Dominik Olszewski .

Editor information

Editors and Affiliations

  1. University of Oviedo, Oviedo, Spain

    Enrique Antonio de la Cal

  2. University of Oviedo, Oviedo, Spain

    José Ramón Villar Flecha

  3. University of A Coruña, Ferrol, Spain

    Héctor Quintián

  4. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Olszewski, D. (2020). An Adaptive Neighborhood Retrieval Visualizer. In: de la Cal, E.A., Villar Flecha, J.R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2020. Lecture Notes in Computer Science(), vol 12344. Springer, Cham. https://doi.org/10.1007/978-3-030-61705-9_4

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  • DOI: https://doi.org/10.1007/978-3-030-61705-9_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61704-2

  • Online ISBN: 978-3-030-61705-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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