Parallel Hybrid SOM Learning on High Dimensional Sparse Data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F47813059%3A19520%2F11%3A%230002011" target="_blank" >RIV/47813059:19520/11:#0002011 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27240/11:86081141
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Parallel Hybrid SOM Learning on High Dimensional Sparse Data
Original language 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 solvedusing parallelization; the speed of proposed algorithm is accelerated using the algorithm version suitable for data collections with certain level of sparsity.
Czech name
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Czech description
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Project
<a href="/en/project/GA205%2F09%2F1079" target="_blank" >GA205/09/1079: Methods of Artificial Inteligence in GIS</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach

Publication year
2011
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Computer Information Systems Analysis and Technologies (CCIS)
ISBN
978-3-642-27244-8
ISSN
1865-0929
e-ISSN
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Number of pages
8
Pages from-to
239-246
Publisher name
Springer
Place of publication
Heidelberg
Event location
Kolkata, India
Event date
Jan 1, 2011
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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