CLUSTERING MODEL OF LOW-STRUCTURED TEXT DATA
Journal Title: Современные информационные технологии и ИТ-образование - Year 2017, Vol 13, Issue 3
Abstract
The article proposes a clustering model for collections of news text messages, as well as the corresponding bubble trap clustering algorithm. The essence of the proposed approach is to divide the entire vector space of text documents into shells of semantic clusters with minimal restrictions on the selection criteria in such a way that the volume of the semantic cluster and the position of its center remain unchanged in the process of adding new vectors to it, and the criterion of affiliation is a given constant accuracy metric.
Authors and Affiliations
Konstantin Otradnov, Dmitry Zhukov, Olga Novikova
Keywords
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- EP ID EP266420
- DOI 10.25559/SITITO.2017.3.439
- Views 120
- Downloads 0
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