Graph visualisation by concurrent differential evolution

Title:

Graph visualisation by concurrent differential evolution

Creator:

Krömer, P., Prílepok, M., Platoš , J., and Snášel, V.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:ac628cfb-3738-4206-a4c4-464d45de8fbf
uuid:ac628cfb-3738-4206-a4c4-464d45de8fbf
doi:10.14311/NNW.2015.25.019

Subject:

defferential evolution, Sammon's projection, and parallel processing

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

A representative dimensionality reduction is an important step in the analysis of real-world data. Vast amounts of raw data are generated by cyberphysical 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-inspired real-parameter optimization methods can be used to implement the Sammon's projection on data from the domain of social networks. This work investigates the ability of several advanced types of the differential evolution algorithm as well as their parallel variants to minimize the error function of the Sammon's projection and compares their results and performance to a traditional heuristic algorithm.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2015 Volume:25 | Number:4

Harvested from:

CDK

Metadata only:

false