A Novel Adaptive Kernel Picture Fuzzy C-Means Clustering Algorithm Based on Grey Wolf Optimizer Algorithm
Over the years, research on fuzzy clustering algorithms has attracted the attention of many researchers, and they have been applied to various areas, such as image segmentation and data clustering. Various fuzzy clustering algorithms have been put forward based on the initial Fuzzy C-Means clusterin...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/14/7/1442 |
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| author | Can-Ming Yang Ye Liu Yi-Ting Wang Yan-Ping Li Wen-Hui Hou Sheng Duan Jian-Qiang Wang |
| author_facet | Can-Ming Yang Ye Liu Yi-Ting Wang Yan-Ping Li Wen-Hui Hou Sheng Duan Jian-Qiang Wang |
| author_sort | Can-Ming Yang |
| collection | DOAJ |
| description | Over the years, research on fuzzy clustering algorithms has attracted the attention of many researchers, and they have been applied to various areas, such as image segmentation and data clustering. Various fuzzy clustering algorithms have been put forward based on the initial Fuzzy C-Means clustering (FCM) with Euclidean distance. However, the existing fuzzy clustering approaches ignore two problems. Firstly, clustering algorithms based on Euclidean distance have a high error rate, and are more sensitive to noise and outliers. Secondly, the parameters of the fuzzy clustering algorithms are hard to determine. In practice, they are often determined by the user’s experience, which results in poor performance of the clustering algorithm. Therefore, considering the above deficiencies, this paper proposes a novel fuzzy clustering algorithm by combining the Gaussian kernel function and Grey Wolf Optimizer (GWO), called Kernel-based Picture Fuzzy C-Means clustering with Grey Wolf Optimizer (KPFCM-GWO). In KPFCM-GWO, the Gaussian kernel function is used as a symmetrical measure of distance between data points and cluster centers, and the GWO is utilized to determine the parameter values of PFCM. To verify the validity of KPFCM-GWO, a comparative study was conducted. The experimental results indicate that KPFCM-GWO outperforms other clustering methods, and the improvement of KPFCM-GWO is mainly attributed to the combination of the Gaussian kernel function and the parameter optimization capability of the GWO. What is more, the paper applies KPFCM-GWO to analyzes the value of an airline’s customers, and five levels of customer categories are defined. |
| first_indexed | 2024-03-09T05:46:47Z |
| format | Article |
| id | doaj.art-1414a52d738a40868b34674f8a24b39f |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-09T05:46:47Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-1414a52d738a40868b34674f8a24b39f2023-12-03T12:20:05ZengMDPI AGSymmetry2073-89942022-07-01147144210.3390/sym14071442A Novel Adaptive Kernel Picture Fuzzy C-Means Clustering Algorithm Based on Grey Wolf Optimizer AlgorithmCan-Ming Yang0Ye Liu1Yi-Ting Wang2Yan-Ping Li3Wen-Hui Hou4Sheng Duan5Jian-Qiang Wang6Library, Guilin University of Technology, Guilin 541004, ChinaSchool of Business, Central South University, Changsha 410083, ChinaSchool of Business, Central South University, Changsha 410083, ChinaSchool of Business, Central South University, Changsha 410083, ChinaSchool of Business, Central South University, Changsha 410083, ChinaCollege of Computer and Artificial Intelligence, Xiangnan University, Chenzhou 423038, ChinaSchool of Business, Central South University, Changsha 410083, ChinaOver the years, research on fuzzy clustering algorithms has attracted the attention of many researchers, and they have been applied to various areas, such as image segmentation and data clustering. Various fuzzy clustering algorithms have been put forward based on the initial Fuzzy C-Means clustering (FCM) with Euclidean distance. However, the existing fuzzy clustering approaches ignore two problems. Firstly, clustering algorithms based on Euclidean distance have a high error rate, and are more sensitive to noise and outliers. Secondly, the parameters of the fuzzy clustering algorithms are hard to determine. In practice, they are often determined by the user’s experience, which results in poor performance of the clustering algorithm. Therefore, considering the above deficiencies, this paper proposes a novel fuzzy clustering algorithm by combining the Gaussian kernel function and Grey Wolf Optimizer (GWO), called Kernel-based Picture Fuzzy C-Means clustering with Grey Wolf Optimizer (KPFCM-GWO). In KPFCM-GWO, the Gaussian kernel function is used as a symmetrical measure of distance between data points and cluster centers, and the GWO is utilized to determine the parameter values of PFCM. To verify the validity of KPFCM-GWO, a comparative study was conducted. The experimental results indicate that KPFCM-GWO outperforms other clustering methods, and the improvement of KPFCM-GWO is mainly attributed to the combination of the Gaussian kernel function and the parameter optimization capability of the GWO. What is more, the paper applies KPFCM-GWO to analyzes the value of an airline’s customers, and five levels of customer categories are defined.https://www.mdpi.com/2073-8994/14/7/1442fuzzy c-means clusteringpicture fuzzy setskernel functiongrey wolf optimizerpicture fuzzy clustering |
| spellingShingle | Can-Ming Yang Ye Liu Yi-Ting Wang Yan-Ping Li Wen-Hui Hou Sheng Duan Jian-Qiang Wang A Novel Adaptive Kernel Picture Fuzzy C-Means Clustering Algorithm Based on Grey Wolf Optimizer Algorithm Symmetry fuzzy c-means clustering picture fuzzy sets kernel function grey wolf optimizer picture fuzzy clustering |
| title | A Novel Adaptive Kernel Picture Fuzzy C-Means Clustering Algorithm Based on Grey Wolf Optimizer Algorithm |
| title_full | A Novel Adaptive Kernel Picture Fuzzy C-Means Clustering Algorithm Based on Grey Wolf Optimizer Algorithm |
| title_fullStr | A Novel Adaptive Kernel Picture Fuzzy C-Means Clustering Algorithm Based on Grey Wolf Optimizer Algorithm |
| title_full_unstemmed | A Novel Adaptive Kernel Picture Fuzzy C-Means Clustering Algorithm Based on Grey Wolf Optimizer Algorithm |
| title_short | A Novel Adaptive Kernel Picture Fuzzy C-Means Clustering Algorithm Based on Grey Wolf Optimizer Algorithm |
| title_sort | novel adaptive kernel picture fuzzy c means clustering algorithm based on grey wolf optimizer algorithm |
| topic | fuzzy c-means clustering picture fuzzy sets kernel function grey wolf optimizer picture fuzzy clustering |
| url | https://www.mdpi.com/2073-8994/14/7/1442 |
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