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Statements

Subject Item
n2:RIV%2F61989100%3A27740%2F13%3A86088262%21RIV14-MSM-27740___
rdf:type
n11:Vysledek skos:Concept
dcterms:description
With increasing opportunities for analyzing large data sources, we have noticed a lack of effective processing in datamining tasks working with large sparse datasets of high dimensions. This work focuses on this issue and on effective clustering using models of artificial intelligence. The authors of this article propose an effective clustering algorithm to exploit the features of neural networks, and especially Self Organizing Maps (SOM), for the reduction of data dimensionality. The issue of computational complexity is resolved by using a parallelization of the standard SOM algorithm. The authors have focused on the acceleration of the presented algorithm using a version suitable for data collections with a certain level of sparsity. Effective acceleration is achieved by improving the winning neuron finding phase and the weight actualization phase. The output presented here demonstrates sufficient acceleration of the standard SOM algorithm while preserving the appropriate accuracy. With increasing opportunities for analyzing large data sources, we have noticed a lack of effective processing in datamining tasks working with large sparse datasets of high dimensions. This work focuses on this issue and on effective clustering using models of artificial intelligence. The authors of this article propose an effective clustering algorithm to exploit the features of neural networks, and especially Self Organizing Maps (SOM), for the reduction of data dimensionality. The issue of computational complexity is resolved by using a parallelization of the standard SOM algorithm. The authors have focused on the acceleration of the presented algorithm using a version suitable for data collections with a certain level of sparsity. Effective acceleration is achieved by improving the winning neuron finding phase and the weight actualization phase. The output presented here demonstrates sufficient acceleration of the standard SOM algorithm while preserving the appropriate accuracy.
dcterms:title
Effective clustering algorithm for high-dimensional sparse data based on SOM Effective clustering algorithm for high-dimensional sparse data based on SOM
skos:prefLabel
Effective clustering algorithm for high-dimensional sparse data based on SOM Effective clustering algorithm for high-dimensional sparse data based on SOM
skos:notation
RIV/61989100:27740/13:86088262!RIV14-MSM-27740___
n11:predkladatel
n14:orjk%3A27740
n3:aktivita
n15:S n15:P
n3:aktivity
P(ED1.1.00/02.0070), S
n3:cisloPeriodika
2
n3:dodaniDat
n8:2014
n3:domaciTvurceVysledku
n5:9491562 n5:3919706 n5:2891409 n5:8939381
n3:druhVysledku
n19:J
n3:duvernostUdaju
n12:S
n3:entitaPredkladatele
n13:predkladatel
n3:idSjednocenehoVysledku
71853
n3:idVysledku
RIV/61989100:27740/13:86088262
n3:jazykVysledku
n18:eng
n3:klicovaSlova
SOM; Parallel computing; Neural networks; Large sparse datasets; High dimension datasets
n3:klicoveSlovo
n6:SOM n6:High%20dimension%20datasets n6:Neural%20networks n6:Large%20sparse%20datasets n6:Parallel%20computing
n3:kodStatuVydavatele
CZ - Česká republika
n3:kontrolniKodProRIV
[9B593FC3B34A]
n3:nazevZdroje
Neural Network World
n3:obor
n9:IN
n3:pocetDomacichTvurcuVysledku
4
n3:pocetTvurcuVysledku
6
n3:projekt
n17:ED1.1.00%2F02.0070
n3:rokUplatneniVysledku
n8:2013
n3:svazekPeriodika
23
n3:tvurceVysledku
Dráždilová, Pavla Slaninová, Kateřina Dvorský, Jiří Vojáček, Lukáš Martinovič, Jan Vondrák, Ivo
n3:wos
000320146300006
s:issn
1210-0552
s:numberOfPages
17
n16:organizacniJednotka
27740