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Experimental Psychology (Russia)

Publisher: Moscow State University of Psychology and Education

ISSN (printed version): 2072-7593

ISSN (online): 2311-7036

DOI: https://doi.org/10.17759/exppsy

License: CC BY-NC 4.0

Started in 2008

Published quarterly

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Stochastic swarm clusterization method in natural language data processing 333

Yuryev G.A.
PhD in Physics and Matematics, Associate Professor, Head of Scientifi c Laboratory, Moscow State University of Psychology & Education, Moscow, Russia
ORCID: https://orcid.org/0000-0002-2960-6562
e-mail: g.a.yuryev@gmail.com

Verkhovskaya E.K.
Researcher, MCUPE, Moscow, Russia
e-mail: katrin636bmw@yandex.ru

Yuryeva N.E.
PhD in Engineering, Research Fellow, Information Technology Center for Psychological-Ecological Studies of the Faculty Newsletter-Technologies, Research Associate, Moscow State University of Psychology and Education, Moscow, Russia
ORCID: https://orcid.org/0000-0003-1419-876X
e-mail: yurieva.ne@gmail.com

Abstract
Consider natural language data processing technology based on non-linear dimensionality reduction method which takes into account the discriminating power of the solution found for given values of the categorical variable associated with each observation. Stochastic optimization method known as the “Particle swarm optimization” is proposed to found characteristics that ensure the best separation of observations in terms of a given quality functional. The basis for evaluating the quality of the solution lies in the purity of the clusters obtained with the k-means method, or with using self-organizing Kohonen feature maps.

Keywords: сombinatorial optimization, particle swarm optimization, non-linear dimensionality reduction

Column: Mathematical Psychology

DOI: https://doi.org/10.17759/exppsy.2018110301

For Reference

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