Evaluation and selection of clustering methods using a hybrid group MCDM

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F19%3A10242773" target="_blank" >RIV/61989100:27510/19:10242773 - isvavai.cz</a>

Result on the web

<a href="https://reader.elsevier.com/reader/sd/pii/S0957417419305135?token=D41C16756DA3F3573623F970AA02971290A7D87A826CED707A2D6890FE262349297E1ED3E76AD18D0B47190E2AA54619" target="_blank" >https://reader.elsevier.com/reader/sd/pii/S0957417419305135?token=D41C16756DA3F3573623F970AA02971290A7D87A826CED707A2D6890FE262349297E1ED3E76AD18D0B47190E2AA54619</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.eswa.2019.07.034" target="_blank" >10.1016/j.eswa.2019.07.034</a>

Result language

angličtina

Original language name

Evaluation and selection of clustering methods using a hybrid group MCDM

Original language description

Due to the lack of objective measures, the evaluation and prioritization of clustering methods is inherently challenging. Since their evaluation generally involves numerous criteria, it can be designed as a multiple criteria decision making (MCDM) problem and using multiple data sets, the problem can be formulated as a group MCDM modeling. In this paper, a MCDM-based framework is proposed to evaluate and rank a number of clustering methods. The proposed approach employs three group MCDM algorithms and a Borda count method which leads to a comprehensive, robust framework capable of evaluating and ranking multiple clustering models on manifold data sets (cases). Moreover, we introduce a hybrid data clustering algorithm which combines a particle swarm optimization (PSO) algorithm with a K-means clustering algorithm. Finally, a clustering comparison with regard to both external and internal evaluation indicators is another contribution of this paper. Six clustering methods are compared based on five evaluation measures. The results of comparative experiments on ten data sets indicate the effectiveness of the proposed hybrid clustering method. More importantly, the experimental results vividly demonstrate the effectiveness of the group MCDM-based evaluation on clustering model selection. (C) 2019 Elsevier Ltd. All rights reserved.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

50202 - Applied Economics, Econometrics

Project

<a href="/en/project/GA17-22662S" target="_blank" >GA17-22662S: Multiple Criteria Decision Making Modelling: Novel Weighting Methods and Hybrid Approaches</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Expert Systems with Applications

ISSN

0957-4174

e-ISSN

—

Volume of the periodical

138

Issue of the periodical within the volume

Prosinec

Country of publishing house

GB - UNITED KINGDOM

Number of pages

19

Pages from-to

—

UT code for WoS article

000489189900014

EID of the result in the Scopus database

—