Multi-objective clustering: a kernel based approach using Differential Evolution
A multi-objective algorithm is always favoured over a single objective algorithm as it considers different aspects of a dataset in the form of various objectives. In this article, a multi-objective clustering algorithm has been proposed based on Differential Evolution. Here, three objectives have be...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2019-07-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2019.1603201 |
| _version_ | 1797684055570907136 |
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| author | Subrat Kumar Nayak Pravat Kumar Rout Alok Kumar Jagadev |
| author_facet | Subrat Kumar Nayak Pravat Kumar Rout Alok Kumar Jagadev |
| author_sort | Subrat Kumar Nayak |
| collection | DOAJ |
| description | A multi-objective algorithm is always favoured over a single objective algorithm as it considers different aspects of a dataset in the form of various objectives. In this article, a multi-objective clustering algorithm has been proposed based on Differential Evolution. Here, three objectives have been considered to handle different complex datasets. In addition to this, a kernel function is hybridised with the objectives to evaluate the data on a hyperspace for reducing the impact of nonlinearity on cluster formation. Moreover, to get the best compromised solution from the Pareto front an effective fuzzy concept has been followed. Five metaheuristic approaches have been taken into consideration for performance comparison. These methodologies have been applied to twelve datasets and the result reveals the efficacy of the proposed model in data clustering. |
| first_indexed | 2024-03-12T00:23:52Z |
| format | Article |
| id | doaj.art-2de0c9693f514f2fb380d5ff6bcf28f9 |
| institution | Directory Open Access Journal |
| issn | 0954-0091 1360-0494 |
| language | English |
| last_indexed | 2024-03-12T00:23:52Z |
| publishDate | 2019-07-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj.art-2de0c9693f514f2fb380d5ff6bcf28f92023-09-15T10:47:58ZengTaylor & Francis GroupConnection Science0954-00911360-04942019-07-0131329432110.1080/09540091.2019.16032011603201Multi-objective clustering: a kernel based approach using Differential EvolutionSubrat Kumar Nayak0Pravat Kumar Rout1Alok Kumar Jagadev2Siksha ‘O’ Anusandhan (Deemed to be University)Siksha ‘O’ Anusandhan (Deemed to be University)KIIT (Deemed to be University)A multi-objective algorithm is always favoured over a single objective algorithm as it considers different aspects of a dataset in the form of various objectives. In this article, a multi-objective clustering algorithm has been proposed based on Differential Evolution. Here, three objectives have been considered to handle different complex datasets. In addition to this, a kernel function is hybridised with the objectives to evaluate the data on a hyperspace for reducing the impact of nonlinearity on cluster formation. Moreover, to get the best compromised solution from the Pareto front an effective fuzzy concept has been followed. Five metaheuristic approaches have been taken into consideration for performance comparison. These methodologies have been applied to twelve datasets and the result reveals the efficacy of the proposed model in data clustering.http://dx.doi.org/10.1080/09540091.2019.1603201multi-objective clusteringdifferential evolutionkernel clusteringpareto front |
| spellingShingle | Subrat Kumar Nayak Pravat Kumar Rout Alok Kumar Jagadev Multi-objective clustering: a kernel based approach using Differential Evolution Connection Science multi-objective clustering differential evolution kernel clustering pareto front |
| title | Multi-objective clustering: a kernel based approach using Differential Evolution |
| title_full | Multi-objective clustering: a kernel based approach using Differential Evolution |
| title_fullStr | Multi-objective clustering: a kernel based approach using Differential Evolution |
| title_full_unstemmed | Multi-objective clustering: a kernel based approach using Differential Evolution |
| title_short | Multi-objective clustering: a kernel based approach using Differential Evolution |
| title_sort | multi objective clustering a kernel based approach using differential evolution |
| topic | multi-objective clustering differential evolution kernel clustering pareto front |
| url | http://dx.doi.org/10.1080/09540091.2019.1603201 |
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