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Combined method for effective clustering based on parallel SOM and spectral clustering

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F11%3A86084592" target="_blank" >RIV/61989100:27240/11:86084592 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Combined method for effective clustering based on parallel SOM and spectral clustering

  • Original language description

    The paper is oriented to the problem of clustering for large datasets with high-dimensions. We propose a two-phase combined method with regard to high dimensions and exploiting the standard clustering algorithm. The first step of the method is based on the learning phase using artificial neural network, especially Self organizing map, which we find as a suitable method for the reduction of the problem complexity. Due to the fact, that the learning phase of artificial neural networks can be time-consuming operation (especially for large highdimensional datasets), we decided to accelerate this phase using parallelization to improve the computational efficiency. The second phase of the proposed method is oriented to clustering. Because the visualization provided by Self organizing maps is depending on the map dimension, and is not as clear and comprehensible in the cases of clustering applications, we decided to use spectral clustering algorithm to obtain sufficient clusters. According to

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

    <a href="/en/project/GA205%2F09%2F1079" target="_blank" >GA205/09/1079: Methods of Artificial Inteligence in GIS</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2011

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    DATESO 2011 : databases, texts, specifications, and objects : proceedings of the Dateso 2011 Workshop

  • ISBN

    978-80-248-2391-1

  • ISSN

  • e-ISSN

  • Number of pages

    12

  • Pages from-to

    120-131

  • Publisher name

    Vysoká škola báňská - Technická univerzita, Fakulta elektrotechniky a informatiky, Katedra informatiky

  • Place of publication

    Ostrava

  • Event location

    Písek

  • Event date

    Apr 20, 2011

  • Type of event by nationality

    CST - Celostátní akce

  • UT code for WoS article