An application of the whale optimization algorithm with Levy flight strategy for clustering of medical datasets
Clustering, which is handled by many researchers, is separating data into clusters without supervision. In clustering, the data are grouped using similarities or differences between them. Many traditional and heuristic algorithms are used in clustering problems and new techniques continue to be deve...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Balikesir University
2021-06-01
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| Series: | An International Journal of Optimization and Control: Theories & Applications |
| Subjects: | |
| Online Access: | http://www.ijocta.org/index.php/files/article/view/1091 |
| _version_ | 1797913441201029120 |
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| author | Ayşe Nagehan Mat Onur İnan Murat Karakoyun |
| author_facet | Ayşe Nagehan Mat Onur İnan Murat Karakoyun |
| author_sort | Ayşe Nagehan Mat |
| collection | DOAJ |
| description | Clustering, which is handled by many researchers, is separating data into clusters without supervision. In clustering, the data are grouped using similarities or differences between them. Many traditional and heuristic algorithms are used in clustering problems and new techniques continue to be developed today. In this study, a new and effective clustering algorithm was developed by using the Whale Optimization Algorithm (WOA) and Levy flight (LF) strategy that imitates the hunting behavior of whales. With the developed WOA-LF algorithm, clustering was performed using ten medical datasets taken from the UCI Machine Learning Repository database. The clustering performance of the WOA-LF was compared with the performance of k-means, k-medoids, fuzzy c-means and the original WOA clustering algorithms. Application results showed that WOA-LF has more successful clustering performance in general and can be used as an alternative algorithm in clustering problems. |
| first_indexed | 2024-04-10T12:11:58Z |
| format | Article |
| id | doaj.art-0806d4ecad574c34a50572671ab27007 |
| institution | Directory Open Access Journal |
| issn | 2146-0957 2146-5703 |
| language | English |
| last_indexed | 2024-04-10T12:11:58Z |
| publishDate | 2021-06-01 |
| publisher | Balikesir University |
| record_format | Article |
| series | An International Journal of Optimization and Control: Theories & Applications |
| spelling | doaj.art-0806d4ecad574c34a50572671ab270072023-02-15T16:15:56ZengBalikesir UniversityAn International Journal of Optimization and Control: Theories & Applications2146-09572146-57032021-06-0111210.11121/ijocta.01.2021.001091An application of the whale optimization algorithm with Levy flight strategy for clustering of medical datasetsAyşe Nagehan Mat0Onur İnan1Murat Karakoyun2Necmettin Erbakan UniversityNecmettin Erbakan UniversityNecmettin Erbakan UniversityClustering, which is handled by many researchers, is separating data into clusters without supervision. In clustering, the data are grouped using similarities or differences between them. Many traditional and heuristic algorithms are used in clustering problems and new techniques continue to be developed today. In this study, a new and effective clustering algorithm was developed by using the Whale Optimization Algorithm (WOA) and Levy flight (LF) strategy that imitates the hunting behavior of whales. With the developed WOA-LF algorithm, clustering was performed using ten medical datasets taken from the UCI Machine Learning Repository database. The clustering performance of the WOA-LF was compared with the performance of k-means, k-medoids, fuzzy c-means and the original WOA clustering algorithms. Application results showed that WOA-LF has more successful clustering performance in general and can be used as an alternative algorithm in clustering problems.http://www.ijocta.org/index.php/files/article/view/1091ClusteringWhale optimization algorithm Levy flight K-means K-medoids Fuzzy c-means |
| spellingShingle | Ayşe Nagehan Mat Onur İnan Murat Karakoyun An application of the whale optimization algorithm with Levy flight strategy for clustering of medical datasets An International Journal of Optimization and Control: Theories & Applications Clustering Whale optimization algorithm Levy flight K-means K-medoids Fuzzy c-means |
| title | An application of the whale optimization algorithm with Levy flight strategy for clustering of medical datasets |
| title_full | An application of the whale optimization algorithm with Levy flight strategy for clustering of medical datasets |
| title_fullStr | An application of the whale optimization algorithm with Levy flight strategy for clustering of medical datasets |
| title_full_unstemmed | An application of the whale optimization algorithm with Levy flight strategy for clustering of medical datasets |
| title_short | An application of the whale optimization algorithm with Levy flight strategy for clustering of medical datasets |
| title_sort | application of the whale optimization algorithm with levy flight strategy for clustering of medical datasets |
| topic | Clustering Whale optimization algorithm Levy flight K-means K-medoids Fuzzy c-means |
| url | http://www.ijocta.org/index.php/files/article/view/1091 |
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