Evaluation and selection of clustering methods using a hybrid group MCDM

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Evaluation and selection of clustering methods using a hybrid group MCDM

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Evaluation and selection of clustering methods using a hybrid group MCDM

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50202 - Applied Economics, Econometrics

Projekt

<a href="/cs/project/GA17-22662S" target="_blank" >GA17-22662S: Modely vícekriteriálního rozhodování: nové metody odhadu vah a hybridní přístupy</a><br>

Návaznosti

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

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

Expert Systems with Applications

ISSN

0957-4174

e-ISSN

—

Svazek periodika

138

Číslo periodika v rámci svazku

Prosinec

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

19

Strana od-do

—

Kód UT WoS článku

000489189900014

EID výsledku v databázi Scopus

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