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Gradient-based elephant herding optimization for cluster analysis
Clustering analysis is essential for obtaining valuable information from a predetermined dataset. However, traditional clustering methods suffer from falling into local optima and an overdependence on the quality of the initial solution. Given these defects, a novel clustering method called gradient...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8795968/ https://www.ncbi.nlm.nih.gov/pubmed/35106027 http://dx.doi.org/10.1007/s10489-021-03020-y |
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author | Duan, Yuxian Liu, Changyun Li, Song Guo, Xiangke Yang, Chunlin |
author_facet | Duan, Yuxian Liu, Changyun Li, Song Guo, Xiangke Yang, Chunlin |
author_sort | Duan, Yuxian |
collection | PubMed |
description | Clustering analysis is essential for obtaining valuable information from a predetermined dataset. However, traditional clustering methods suffer from falling into local optima and an overdependence on the quality of the initial solution. Given these defects, a novel clustering method called gradient-based elephant herding optimization for cluster analysis (GBEHO) is proposed. A well-defined set of heuristics is introduced to select the initial centroids instead of selecting random initial points. Specifically, the elephant optimization algorithm (EHO) is combined with the gradient-based algorithm GBO for assigning initial cluster centers across the search space. Second, to overcome the imbalance between the original EHO exploration and exploitation, the initialized population is improved by introducing Gaussian chaos mapping. In addition, two operators, i.e., random wandering and variation operators, are set to adjust the location update strategy of the agents. Nine datasets from synthetic and real-world datasets are adopted to evaluate the effectiveness of the proposed algorithm and the other metaheuristic algorithms. The results show that the proposed algorithm ranks first among the 10 algorithms. It is also extensively compared with state-of-the-art techniques, and four evaluation criteria of accuracy rate, specificity, detection rate, and F-measure are used. The obtained results clearly indicate the excellent performance of GBEHO, while the stability is also more prominent. |
format | Online Article Text |
id | pubmed-8795968 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-87959682022-01-28 Gradient-based elephant herding optimization for cluster analysis Duan, Yuxian Liu, Changyun Li, Song Guo, Xiangke Yang, Chunlin Appl Intell (Dordr) Article Clustering analysis is essential for obtaining valuable information from a predetermined dataset. However, traditional clustering methods suffer from falling into local optima and an overdependence on the quality of the initial solution. Given these defects, a novel clustering method called gradient-based elephant herding optimization for cluster analysis (GBEHO) is proposed. A well-defined set of heuristics is introduced to select the initial centroids instead of selecting random initial points. Specifically, the elephant optimization algorithm (EHO) is combined with the gradient-based algorithm GBO for assigning initial cluster centers across the search space. Second, to overcome the imbalance between the original EHO exploration and exploitation, the initialized population is improved by introducing Gaussian chaos mapping. In addition, two operators, i.e., random wandering and variation operators, are set to adjust the location update strategy of the agents. Nine datasets from synthetic and real-world datasets are adopted to evaluate the effectiveness of the proposed algorithm and the other metaheuristic algorithms. The results show that the proposed algorithm ranks first among the 10 algorithms. It is also extensively compared with state-of-the-art techniques, and four evaluation criteria of accuracy rate, specificity, detection rate, and F-measure are used. The obtained results clearly indicate the excellent performance of GBEHO, while the stability is also more prominent. Springer US 2022-01-28 2022 /pmc/articles/PMC8795968/ /pubmed/35106027 http://dx.doi.org/10.1007/s10489-021-03020-y Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Duan, Yuxian Liu, Changyun Li, Song Guo, Xiangke Yang, Chunlin Gradient-based elephant herding optimization for cluster analysis |
title | Gradient-based elephant herding optimization for cluster analysis |
title_full | Gradient-based elephant herding optimization for cluster analysis |
title_fullStr | Gradient-based elephant herding optimization for cluster analysis |
title_full_unstemmed | Gradient-based elephant herding optimization for cluster analysis |
title_short | Gradient-based elephant herding optimization for cluster analysis |
title_sort | gradient-based elephant herding optimization for cluster analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8795968/ https://www.ncbi.nlm.nih.gov/pubmed/35106027 http://dx.doi.org/10.1007/s10489-021-03020-y |
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