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Statements

Subject Item
n2:RIV%2F61989100%3A27240%2F11%3A86084592%21RIV13-GA0-27240___
rdf:type
skos:Concept n8:Vysledek
dcterms: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.
dcterms: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
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
skos:notation
RIV/61989100:27240/11:86084592!RIV13-GA0-27240___
n8:predkladatel
n9:orjk%3A27240
n4:aktivita
n16:P
n4:aktivity
P(GA205/09/1079)
n4:dodaniDat
n11:2013
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n7:2891409 n7:6108229 n7:8939381 n7:9491562 n7:1923099
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n20:D
n4:duvernostUdaju
n14:S
n4:entitaPredkladatele
n17:predkladatel
n4:idSjednocenehoVysledku
190890
n4:idVysledku
RIV/61989100:27240/11:86084592
n4:jazykVysledku
n21:eng
n4:klicovaSlova
Spectral clustering; Second phase; Problem complexity; Parallelizations; Learning phase; Large datasets; High-dimensional; High dimensions; Data sets; Combined method; Clustering applications
n4:klicoveSlovo
n5:High%20dimensions n5:High-dimensional n5:Data%20sets n5:Combined%20method n5:Clustering%20applications n5:Large%20datasets n5:Learning%20phase n5:Spectral%20clustering n5:Second%20phase n5:Problem%20complexity n5:Parallelizations
n4:kontrolniKodProRIV
[3FCFC628DF6F]
n4:mistoKonaniAkce
Písek
n4:mistoVydani
Ostrava
n4:nazevZdroje
DATESO 2011 : databases, texts, specifications, and objects : proceedings of the Dateso 2011 Workshop
n4:obor
n13:IN
n4:pocetDomacichTvurcuVysledku
5
n4:pocetTvurcuVysledku
5
n4:projekt
n22:GA205%2F09%2F1079
n4:rokUplatneniVysledku
n11:2011
n4:tvurceVysledku
Slaninová, Kateřina Dráždilová, Pavla Dvorský, Jiří Vojáček, Lukáš Martinovič, Jan
n4:typAkce
n18:CST
n4:zahajeniAkce
2011-04-20+02:00
s:numberOfPages
12
n15:hasPublisher
Vysoká škola báňská - Technická univerzita, Fakulta elektrotechniky a informatiky, Katedra informatiky
n10:isbn
978-80-248-2391-1
n19:organizacniJednotka
27240