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

Kód výsledku v 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>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

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

Popis výsledku anglicky

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

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GA205%2F09%2F1079" target="_blank" >GA205/09/1079: Metody umělé inteligence v GIS</a><br>

Návaznosti

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

Rok uplatnění

2011

Kód důvěrnosti údajů

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

Název statě ve sborníku

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

ISBN

978-80-248-2391-1

ISSN

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e-ISSN

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Počet stran výsledku

12

Strana od-do

120-131

Název nakladatele

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

Místo vydání

Ostrava

Místo konání akce

Písek

Datum konání akce

20. 4. 2011

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

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