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Statements

Subject Item
n2:RIV%2F61989100%3A27240%2F11%3A86081141%21RIV13-GA0-27240___
rdf:type
skos:Concept n14:Vysledek
dcterms:description
Self organizing maps (also called Kohonen maps) are known for their capability of projecting high-dimensional space into lower dimensions. There are commonly discussed problems like rapidly increased computational complexity or specific similarity representation in the high-dimensional space. In the paper there is proposed the effective clustering algorithm based on self organizing map with the main purpose to reduce high dimension of the input dataset. The problem of computational complexity is solved using parallelization; the speed of proposed algorithm is accelerated using the algorithm version suitable for data collections with certain level of sparsity. Self organizing maps (also called Kohonen maps) are known for their capability of projecting high-dimensional space into lower dimensions. There are commonly discussed problems like rapidly increased computational complexity or specific similarity representation in the high-dimensional space. In the paper there is proposed the effective clustering algorithm based on self organizing map with the main purpose to reduce high dimension of the input dataset. The problem of computational complexity is solved using parallelization; the speed of proposed algorithm is accelerated using the algorithm version suitable for data collections with certain level of sparsity.
dcterms:title
Parallel hybrid SOM learning on high dimensional sparse data Parallel hybrid SOM learning on high dimensional sparse data
skos:prefLabel
Parallel hybrid SOM learning on high dimensional sparse data Parallel hybrid SOM learning on high dimensional sparse data
skos:notation
RIV/61989100:27240/11:86081141!RIV13-GA0-27240___
n14:predkladatel
n23:orjk%3A27240
n3:aktivita
n8:S n8:P
n3:aktivity
P(GA205/09/1079), S
n3:dodaniDat
n9:2013
n3:domaciTvurceVysledku
n5:8939381 n5:3919706 n5:9491562 n5:2891409
n3:druhVysledku
n16:D
n3:duvernostUdaju
n21:S
n3:entitaPredkladatele
n18:predkladatel
n3:idSjednocenehoVysledku
219505
n3:idVysledku
RIV/61989100:27240/11:86081141
n3:jazykVysledku
n15:eng
n3:klicovaSlova
Sparse data; Similarity representation; Parallelizations; High-dimensional; High dimensions; High dimensional spaces; Data sets; Data collection
n3:klicoveSlovo
n4:Similarity%20representation n4:Sparse%20data n4:Parallelizations n4:Data%20sets n4:High%20dimensional%20spaces n4:Data%20collection n4:High-dimensional n4:High%20dimensions
n3:kontrolniKodProRIV
[44E4F5140F4E]
n3:mistoKonaniAkce
Kalkata
n3:mistoVydani
Dordrecht
n3:nazevZdroje
Computer Information Systems - Analysis and Technologies international conference, CISIM 2011 : proceedings
n3:obor
n13:IN
n3:pocetDomacichTvurcuVysledku
4
n3:pocetTvurcuVysledku
5
n3:projekt
n22:GA205%2F09%2F1079
n3:rokUplatneniVysledku
n9:2011
n3:tvurceVysledku
Dvorský, Jiří Martinovič, Jan Vojáček, Lukáš Slaninová, Kateřina Vondrák, Ivo
n3:typAkce
n19:WRD
n3:zahajeniAkce
2011-12-14+01:00
s:issn
1865-0929
s:numberOfPages
8
n12:doi
10.1007/978-3-642-27245-5_29
n11:hasPublisher
Springer-Verlag
n17:isbn
978-3-642-27244-8
n20:organizacniJednotka
27240