Empirical study on unsupervised feature selection for document clustering

Mackutė-Varoneckienė, Aušra; Krilavičius, Tomas

Type of publication:

Straipsnis konferencijos medžiagoje Clarivate Analytics Web of Science ar/ir Scopus / Article in Clarivate Analytics Web of Science or Scopus DB conference proceedings (P1a)

Field of Science:

Informatika / Informatics (N009)

Author(s):

Mackutė-Varoneckienė, Aušra;Krilavičius, Tomas

Title:

Empirical study on unsupervised feature selection for document clustering

Is part of:

Human language technologies - the Baltic perspective : proceedings of the 6th international conference, Baltic HLT 2014. Amsterdam : IOS Press, 2014

Extent:

p. 107-110

Date:

2014

Series/Report no.:

(Frontiers in artificial intelligence and applications. Vol. 268 0922-6389)

Keywords:

Klasterizavimas;Text mining;Feature selection;Text clustering

ISBN:

9781614994411

Abstract:

Unsupervised feature selection is very important in the document clustering process. This paper presents the empirical research on feature selection as well as clustering methods and feature representation suitability for Lithuanian and Russian document clustering

Internet:

http://ebooks.iospress.nl/volumearticle/38012

Affiliation(s):

Baltijos pažangių technologijų institutas
Informatikos fakultetas
Taikomosios informatikos katedra
Vytauto Didžiojo universitetas

Appears in Collections:

Universiteto mokslo publikacijos / University Research Publications

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