GRAPH VISUALISATION BY CONCURRENT DIFFERENTIAL EVOLUTION

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F15%3A86096581" target="_blank" >RIV/61989100:27740/15:86096581 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.14311/NNW.2015.25.019" target="_blank" >http://dx.doi.org/10.14311/NNW.2015.25.019</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14311/NNW.2015.25.019" target="_blank" >10.14311/NNW.2015.25.019</a>

Result language

angličtina

Original language name

GRAPH VISUALISATION BY CONCURRENT DIFFERENTIAL EVOLUTION

Original language description

A representative dimensionality reduction is an important step in the analysis of real-world data. Vast amounts of raw data are generated by cyber-physical and information systems in different domains. They often feature A combination of high dimensionality, large volume, and vague, loosely defined structure. The main goal of visual data analysis is an intuitive, comprehensible, efficient, and graphically appealing representation of information and knowledge that can be found in such collections. In order to achieve an efficient visualisation, raw data need to be transformed into a refined form suitable for machine and human analysis. Various methods of dimension reduction and projection to low-dimensional spaces are used to accomplish this task. Sammon's projection is a well-known non-linear projection algorithm valued for its ability to preserve dependencies from an original high-dimensional data space in the low-dimensional projection space. Recently, it has been shown that bio-insp

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

IN - Informatics

OECD FORD branch

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Project

<a href="/en/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: IT4Innovations Centre of Excellence</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2015

Confidentiality

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

Name of the periodical

Neural Network World

ISSN

1210-0552

e-ISSN

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Volume of the periodical

25

Issue of the periodical within the volume

4

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

7

Pages from-to

369-386

UT code for WoS article

000361503300002

EID of the result in the Scopus database

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