Growing neural gas based on data density

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F18%3A10240396" target="_blank" >RIV/61989100:27240/18:10240396 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/18:10240396

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007%2F978-3-319-99954-8_27" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-319-99954-8_27</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-99954-8_27" target="_blank" >10.1007/978-3-319-99954-8_27</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Growing neural gas based on data density

Popis výsledku v původním jazyce

The size, complexity and dimensionality of data collections are ever increasing from the beginning of the computer era. Clustering methods, such as Growing Neural Gas (GNG) [10] that is based on unsupervised learning, is used to reveal structures and to reduce large amounts of raw data. The growth of computational complexity of such clustering method, caused by growing data dimensionality and the specific similarity measurement in a high-dimensional space, reduces the effectiveness of clustering method in many real applications. The growth of computational complexity can be partially solved using the parallel computation facilities, such as High Performance Computing (HPC) cluster with MPI. An effective parallel implementation of GNG is discussed in this paper, while the main focus is on minimizing of interprocess communication which depends on the number of neurons and edges among neurons in the neural network. A new algorithm of adding neurons depending on data density is proposed in the paper. (C) Springer Nature Switzerland AG 2018.

Název v anglickém jazyce

Growing neural gas based on data density

Popis výsledku anglicky

The size, complexity and dimensionality of data collections are ever increasing from the beginning of the computer era. Clustering methods, such as Growing Neural Gas (GNG) [10] that is based on unsupervised learning, is used to reveal structures and to reduce large amounts of raw data. The growth of computational complexity of such clustering method, caused by growing data dimensionality and the specific similarity measurement in a high-dimensional space, reduces the effectiveness of clustering method in many real applications. The growth of computational complexity can be partially solved using the parallel computation facilities, such as High Performance Computing (HPC) cluster with MPI. An effective parallel implementation of GNG is discussed in this paper, while the main focus is on minimizing of interprocess communication which depends on the number of neurons and edges among neurons in the neural network. A new algorithm of adding neurons depending on data density is proposed in the paper. (C) Springer Nature Switzerland AG 2018.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/LM2015070" target="_blank" >LM2015070: IT4Innovations národní superpočítačové centrum</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Lecture Notes in Computer Science. Volume 11127

ISBN

978-3-319-99953-1

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

10

Strana od-do

314-323

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Olomouc

Datum konání akce

27. 9. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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