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Evaluation and selection of clustering methods using a hybrid group MCDM

Evaluation and selection of clustering methods using a hybrid group MCDM
Evaluation and selection of clustering methods using a hybrid group MCDM
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.
0957-4174
Barak, Sasan
f82186de-f5b7-4224-9621-a00e7501f2c3
Mokfi, Taha
180da17e-e3bc-406d-b16b-d4a89fdee196
Barak, Sasan
f82186de-f5b7-4224-9621-a00e7501f2c3
Mokfi, Taha
180da17e-e3bc-406d-b16b-d4a89fdee196

Barak, Sasan and Mokfi, Taha (2019) Evaluation and selection of clustering methods using a hybrid group MCDM. Expert Systems with Applications, 138. (doi:10.1016/j.eswa.2019.07.034).

Record type: Article

Abstract

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.

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More information

Accepted/In Press date: 14 July 2019
e-pub ahead of print date: 15 July 2019
Published date: 30 December 2019

Identifiers

Local EPrints ID: 434853
URI: https://eprints.soton.ac.uk/id/eprint/434853
ISSN: 0957-4174
PURE UUID: 298b82b1-e555-439f-b4fa-834cb766b479
ORCID for Sasan Barak: ORCID iD orcid.org/0000-0001-7715-9958

Catalogue record

Date deposited: 11 Oct 2019 16:30
Last modified: 12 Oct 2019 00:20

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Contributors

Author: Sasan Barak ORCID iD
Author: Taha Mokfi

University divisions

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