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The Use of Conventional Clustering Methods Combined with SOM to Increase the Efficiency

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F21%3AA22026UY" target="_blank" >RIV/61988987:17310/21:A22026UY - isvavai.cz</a>

  • Result on the web

    <a href="https://link.springer.com/article/10.1007%2Fs00521-021-06251-9" target="_blank" >https://link.springer.com/article/10.1007%2Fs00521-021-06251-9</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/s00521-021-06251-9" target="_blank" >10.1007/s00521-021-06251-9</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    The Use of Conventional Clustering Methods Combined with SOM to Increase the Efficiency

  • Original language description

    This article reflects research in the field of artificial intelligence and demonstrates a higher efficiency achievement of conventional clustering methods in combination with unconventional methods. It concerns a new hybrid approach based on the SOM (Self Organizing Maps) method. We focused on the possibility of combining SOM with other clustering methods - CLARA, CURE a K-means. Method SOM is primarily useful in the first phases of the process, where knowledge of the data is too vague. It is thus followed by the use of a selected clustering algorithm. It then works with preprocessed data. Its performance, compared with its outputs on unprocessed data, is more efficient, which is proved by the performed experimental study on the benchmark data set Fundamental Clustering Problems Suite (FCPS). Part of the experimental verification was also a comparison of the achieved outputs with other approaches using this dataset based on a standard metrics - Rand index.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10200 - Computer and information sciences

Result continuities

  • Project

    <a href="/en/project/TL02000313" target="_blank" >TL02000313: Intelligent neuro-rehabilitation system for patients with acquired brain damage in early stages of treatment</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2021

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    NEURAL COMPUT APPL

  • ISSN

    0941-0643

  • e-ISSN

    1433-3058

  • Volume of the periodical

  • Issue of the periodical within the volume

    JUN 2021

  • Country of publishing house

    GB - UNITED KINGDOM

  • Number of pages

    13

  • Pages from-to

  • UT code for WoS article

    000665754900001

  • EID of the result in the Scopus database

    2-s2.0-85108601497