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An Approach to Feature Space Construction from Clustering Feature Tree

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Artificial Intelligence (RCAI 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 934))

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Abstract

Generally, clustering feature tree consists of nodes given as vectors. In case of non-vector nodes a transformation into feature vectors is needed. Feature extraction algorithm determines the volume and quality of information enclosed in features and quality of clustering. Thus this kind of transformation is important part of clustering procedure. In this paper an approach to clustering feature space construction from clustering feature tree is proposed. Presented approach allows to save hierarchy information and reduce feature space dimension. An efficiency of proposed approach is shown in the experiment part with different clustering algorithms. Result analysis is provided at the end of the paper.

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Acknowledgment

This study was supported Ministry of Education and Science of Russia in framework of project No 2.1182.2017/4.6 and Russian Foundation of base Research in framework of project No 16-47-732120 r_ofi_m.

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Authors and Affiliations

  1. Ulyanovsk State Technical University, Ulyanovksk, 432027, Russian Federation

    Pavel Dudarin, Mikhail Samokhvalov & Nadezhda Yarushkina

Authors
  1. Pavel Dudarin
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  2. Mikhail Samokhvalov
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  3. Nadezhda Yarushkina
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Corresponding author

Correspondence to Pavel Dudarin .

Editor information

Editors and Affiliations

  1. Department of Data Analysis and Artificial Intelligence, National Research University Higher School of Economics, Moscow, Russia

    Sergei O. Kuznetsov

  2. Federal Research Center Computer Science and Control, Institute of Informatics Problems, Moscow, Russia

    Gennady S. Osipov

  3. Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia

    Vadim L. Stefanuk

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Dudarin, P., Samokhvalov, M., Yarushkina, N. (2018). An Approach to Feature Space Construction from Clustering Feature Tree. In: Kuznetsov, S., Osipov, G., Stefanuk, V. (eds) Artificial Intelligence. RCAI 2018. Communications in Computer and Information Science, vol 934. Springer, Cham. https://doi.org/10.1007/978-3-030-00617-4_17

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  • DOI: https://doi.org/10.1007/978-3-030-00617-4_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00616-7

  • Online ISBN: 978-3-030-00617-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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