Vestnik KRAUNC. Fiziko-Matematicheskie Nauki
RUS  ENG    JOURNALS   PEOPLE   ORGANISATIONS   CONFERENCES   SEMINARS   VIDEO LIBRARY   PACKAGE AMSBIB  
General information
Latest issue
Archive
Guidelines for authors
Submit a manuscript

Search papers
Search references

RSS
Latest issue
Current issues
Archive issues
What is RSS



Vestnik KRAUNC. Fiz.-Mat. Nauki:
Year:
Volume:
Issue:
Page:
Find






Personal entry:
Login:
Password:
Save password
Enter
Forgotten password?
Register


Vestnik KRAUNC. Fiziko-Matematicheskie Nauki, 2022, Volume 39, Number 2, Pages 136–149
DOI: https://doi.org/10.26117/2079-6641-2022-39-2-136-149
(Mi vkam543)
 

INFORMATION AND COMPUTATION TECHNOLOGIES

Clustering algorithm based on feature space partitioning

M. A. Kazakov

Institute of Applied Mathematics and Automation KBSC RAS
References:
Abstract: A new approach to robust clustering is proposed based on recursive partitioning of the feature space and density analysis. An algorithm for robust clustering of linearly inseparable points, its software implementation, as well as test results on classical data distributions are presented.
Keywords: clustering, robust clustering, machine learning.
Document Type: Article
UDC: 519.7
MSC: 68Т27
Language: Russian
Citation: M. A. Kazakov, “Clustering algorithm based on feature space partitioning”, Vestnik KRAUNC. Fiz.-Mat. Nauki, 39:2 (2022), 136–149
Citation in format AMSBIB
\Bibitem{Kaz22}
\by M.~A.~Kazakov
\paper Clustering algorithm based on feature space partitioning
\jour Vestnik KRAUNC. Fiz.-Mat. Nauki
\yr 2022
\vol 39
\issue 2
\pages 136--149
\mathnet{http://mi.mathnet.ru/vkam543}
\crossref{https://doi.org/10.26117/2079-6641-2022-39-2-136-149}
Linking options:
  • https://www.mathnet.ru/eng/vkam543
  • https://www.mathnet.ru/eng/vkam/v39/i2/p136
  • Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
    Vestnik KRAUNC. Fiziko-Matematicheskie Nauki Vestnik KRAUNC. Fiziko-Matematicheskie Nauki
    Statistics & downloads:
    Abstract page:69
    Full-text PDF :68
    References:16
     
      Contact us:
     Terms of Use  Registration to the website  Logotypes © Steklov Mathematical Institute RAS, 2024