Construction of an optimal collective decision in cluster analysis on the basis of an averaged co-association matrix and cluster validity indices

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Construction of an optimal collective decision in cluster analysis on the basis of an averaged co-association matrix and cluster validity indices. / Berikov, V. B.

в: Pattern Recognition and Image Analysis, Том 27, № 2, 01.04.2017, стр. 153-165.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

BibTeX

@article{faa1bb4f1d554c3cb47ae82c0c8721b7,

title = "Construction of an optimal collective decision in cluster analysis on the basis of an averaged co-association matrix and cluster validity indices",

abstract = "An ensemble clustering method is proposed that is based on a weight averaged co-association matrix. The ensemble includes various cluster analysis algorithms whose weights are calculated with the use of cluster validity indices. The properties of the ensemble are analyzed, a probabilistic model is described by which the relations between the characteristics of the ensemble and a quality estimate of a decision are determined, and a method is proposed for determining the optimal weights. The efficiency of the method is analyzed by statistical simulation.",

keywords = "classification, cluster analysis, cluster validity index, collective decision, ensemble of algorithms, latent classes, recognition model",

author = "Berikov, {V. B.}",

year = "2017",

month = apr,

day = "1",

doi = "10.1134/S1054661816040040",

language = "English",

volume = "27",

pages = "153--165",

journal = "Pattern Recognition and Image Analysis",

issn = "1054-6618",

publisher = "Maik Nauka Publishing / Springer SBM",

number = "2",

}

RIS

TY - JOUR

T1 - Construction of an optimal collective decision in cluster analysis on the basis of an averaged co-association matrix and cluster validity indices

AU - Berikov, V. B.

PY - 2017/4/1

Y1 - 2017/4/1

N2 - An ensemble clustering method is proposed that is based on a weight averaged co-association matrix. The ensemble includes various cluster analysis algorithms whose weights are calculated with the use of cluster validity indices. The properties of the ensemble are analyzed, a probabilistic model is described by which the relations between the characteristics of the ensemble and a quality estimate of a decision are determined, and a method is proposed for determining the optimal weights. The efficiency of the method is analyzed by statistical simulation.

AB - An ensemble clustering method is proposed that is based on a weight averaged co-association matrix. The ensemble includes various cluster analysis algorithms whose weights are calculated with the use of cluster validity indices. The properties of the ensemble are analyzed, a probabilistic model is described by which the relations between the characteristics of the ensemble and a quality estimate of a decision are determined, and a method is proposed for determining the optimal weights. The efficiency of the method is analyzed by statistical simulation.

KW - classification

KW - cluster analysis

KW - cluster validity index

KW - collective decision

KW - ensemble of algorithms

KW - latent classes

KW - recognition model

UR - http://www.scopus.com/inward/record.url?scp=85020811618&partnerID=8YFLogxK

U2 - 10.1134/S1054661816040040

DO - 10.1134/S1054661816040040

M3 - Article

AN - SCOPUS:85020811618

VL - 27

SP - 153

EP - 165

JO - Pattern Recognition and Image Analysis

JF - Pattern Recognition and Image Analysis

SN - 1054-6618

IS - 2

ER -

ID: 10040167