TY - GEN

T1 - Agglomerative Method for Texts Clustering

AU - Orekhov, Andrey V.

N1 - Conference code: 5th

PY - 2019

Y1 - 2019

N2 - Usually, text documents are represented as a vector of n-dimensional Euclidean space. One of the main it the problem of the typology of texts using cluster analysis is to determine the number of clusters. In this article was researched the agglomerative clustering algorithm in Euclidean space. A statistical criterion for completing the clustering process was deriving as the Markov moment. Was considered the problem of cluster stability. As an example, it was considered retrieval of the harmful content.

AB - Usually, text documents are represented as a vector of n-dimensional Euclidean space. One of the main it the problem of the typology of texts using cluster analysis is to determine the number of clusters. In this article was researched the agglomerative clustering algorithm in Euclidean space. A statistical criterion for completing the clustering process was deriving as the Markov moment. Was considered the problem of cluster stability. As an example, it was considered retrieval of the harmful content.

KW - Cluster analysis

KW - Clustering method

KW - Euclidean space

KW - Harmful content

KW - Least squares method

KW - Markov moment

KW - Cluster analysis, Clustering method, Least squares method, Euclidean space, Markov moment, Harmful content .

UR - http://www.scopus.com/inward/record.url?scp=85065304968&partnerID=8YFLogxK

UR - http://www.mendeley.com/research/agglomerative-method-texts-clustering

U2 - 10.1007/978-3-030-17705-8_2

DO - 10.1007/978-3-030-17705-8_2

M3 - статья в сборнике материалов конференции

AN - SCOPUS:85065304968

SN - 9783030177041

T3 - Lecture Notes in Computer Science

SP - 19

EP - 32

BT - Internet Science - INSCI 2018 International Workshops

A2 - Bodrunova, S.S.

A2 - et al.,

PB - Springer Nature

Y2 - 24 October 2018 through 26 October 2018

ER -