Effective Clustering Algorithm for High-Dimensional Sparse Data Based on SOM
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F47813059%3A19520%2F13%3A%230002442" target="_blank" >RIV/47813059:19520/13:#0002442 - isvavai.cz</a>
Nalezeny alternativní kódy
RIV/61989100:27740/13:86088262
Výsledek na webu
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DOI - Digital Object Identifier
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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Effective Clustering Algorithm for High-Dimensional Sparse Data Based on SOM
Popis výsledku v původním jazyce
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.
Název v anglickém jazyce
Effective Clustering Algorithm for High-Dimensional Sparse Data Based on SOM
Popis výsledku anglicky
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.
Klasifikace
Druh
J<sub>x</sub> - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)
CEP obor
IN - Informatika
OECD FORD obor
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Návaznosti výsledku
Projekt
<a href="/cs/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: Centrum excelence IT4Innovations</a><br>
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2013
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
NEURAL NETWORK WORLD
ISSN
1210-0552
e-ISSN
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Svazek periodika
23
Číslo periodika v rámci svazku
2
Stát vydavatele periodika
CZ - Česká republika
Počet stran výsledku
7
Strana od-do
131-147
Kód UT WoS článku
000320146300006
EID výsledku v databázi Scopus
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