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

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
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 our results, the proposed combined method is sufficiently rapid and quite accurate. 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 our results, the proposed combined method is sufficiently rapid and quite accurate. (en)
Title	Combined method for effective clustering based on parallel SOM and spectral clustering Combined method for effective clustering based on parallel SOM and spectral clustering (en)
skos:prefLabel	Combined method for effective clustering based on parallel SOM and spectral clustering Combined method for effective clustering based on parallel SOM and spectral clustering (en)
skos:notation	RIV/61989100:27240/11:86084592!RIV13-GA0-27240___
http://linked.open...avai/riv/aktivita	P
http://linked.open...avai/riv/aktivity	P(GA205/09/1079)
http://linked.open...vai/riv/dodaniDat	2013
http://linked.open...aciTvurceVysledku	Dráždilová, Pavla Vojáček, Lukáš Slaninová, Kateřina Dvorský, Jiří Martinovič, Jan
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Vysoká škola báňská - Technická univerzita Ostrava / Fakulta elektrotechniky a informatiky
http://linked.open...dnocenehoVysledku	190890
http://linked.open...ai/riv/idVysledku	RIV/61989100:27240/11:86084592
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Spectral clustering; Second phase; Problem complexity; Parallelizations; Learning phase; Large datasets; High-dimensional; High dimensions; Data sets; Combined method; Clustering applications (en)
http://linked.open.../riv/klicoveSlovo	Clustering applications Combined method Data sets High dimensions High-dimensional Large datasets Learning phase Parallelizations Problem complexity Second phase Spectral clustering
http://linked.open...ontrolniKodProRIV	[3FCFC628DF6F]
http://linked.open...v/mistoKonaniAkce	Písek
http://linked.open...i/riv/mistoVydani	Ostrava
http://linked.open...i/riv/nazevZdroje	DATESO 2011 : databases, texts, specifications, and objects : proceedings of the Dateso 2011 Workshop
http://linked.open...in/vavai/riv/obor	IN
http://linked.open...ichTvurcuVysledku	5 (xsd:int)
http://linked.open...cetTvurcuVysledku	5 (xsd:int)
http://linked.open...vavai/riv/projekt	Methods of Artificial Inteligence in GIS
http://linked.open...UplatneniVysledku	2011
http://linked.open...iv/tvurceVysledku	Dráždilová, Pavla Dvorský, Jiří Martinovič, Jan Slaninová, Kateřina Vojáček, Lukáš
http://linked.open...vavai/riv/typAkce	CST - Celostátní
http://linked.open.../riv/zahajeniAkce	2011-04-20 (xsd:date)
number of pages	12 (xsd:int)
http://purl.org/ne...btex#hasPublisher	Vysoká škola báňská - Technická univerzita, Fakulta elektrotechniky a informatiky, Katedra informatiky
https://schema.org/isbn	978-80-248-2391-1
http://localhost/t...ganizacniJednotka	27240

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