Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients
Clustering is an unsupervised machine learning technique with many practical applications that has gathered extensive research interest. Aside from deterministic or probabilistic techniques, fuzzy C-means clustering (FCM) is also a common clustering technique. Since the advent of the FCM method, man...
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MDPI AG
2020-06-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/13/7/158 |
| _version_ | 1797563752811331584 |
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| author | Tran Dinh Khang Nguyen Duc Vuong Manh-Kien Tran Michael Fowler |
| author_facet | Tran Dinh Khang Nguyen Duc Vuong Manh-Kien Tran Michael Fowler |
| author_sort | Tran Dinh Khang |
| collection | DOAJ |
| description | Clustering is an unsupervised machine learning technique with many practical applications that has gathered extensive research interest. Aside from deterministic or probabilistic techniques, fuzzy C-means clustering (FCM) is also a common clustering technique. Since the advent of the FCM method, many improvements have been made to increase clustering efficiency. These improvements focus on adjusting the membership representation of elements in the clusters, or on fuzzifying and defuzzifying techniques, as well as the distance function between elements. This study proposes a novel fuzzy clustering algorithm using multiple different fuzzification coefficients depending on the characteristics of each data sample. The proposed fuzzy clustering method has similar calculation steps to FCM with some modifications. The formulas are derived to ensure convergence. The main contribution of this approach is the utilization of multiple fuzzification coefficients as opposed to only one coefficient in the original FCM algorithm. The new algorithm is then evaluated with experiments on several common datasets and the results show that the proposed algorithm is more efficient compared to the original FCM as well as other clustering methods. |
| first_indexed | 2024-03-10T18:47:51Z |
| format | Article |
| id | doaj.art-d392a0187b8f493dbdfdb10ddb2c9205 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-10T18:47:51Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-d392a0187b8f493dbdfdb10ddb2c92052023-11-20T05:24:05ZengMDPI AGAlgorithms1999-48932020-06-0113715810.3390/a13070158Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification CoefficientsTran Dinh Khang0Nguyen Duc Vuong1Manh-Kien Tran2Michael Fowler3Department of Information Systems, Hanoi University of Science and Technology, Hanoi 10000, VietnamDepartment of Information Systems, Hanoi University of Science and Technology, Hanoi 10000, VietnamDepartment of Chemical Engineering, University of Waterloo, Waterloo, ON N2L 3G1, CanadaDepartment of Chemical Engineering, University of Waterloo, Waterloo, ON N2L 3G1, CanadaClustering is an unsupervised machine learning technique with many practical applications that has gathered extensive research interest. Aside from deterministic or probabilistic techniques, fuzzy C-means clustering (FCM) is also a common clustering technique. Since the advent of the FCM method, many improvements have been made to increase clustering efficiency. These improvements focus on adjusting the membership representation of elements in the clusters, or on fuzzifying and defuzzifying techniques, as well as the distance function between elements. This study proposes a novel fuzzy clustering algorithm using multiple different fuzzification coefficients depending on the characteristics of each data sample. The proposed fuzzy clustering method has similar calculation steps to FCM with some modifications. The formulas are derived to ensure convergence. The main contribution of this approach is the utilization of multiple fuzzification coefficients as opposed to only one coefficient in the original FCM algorithm. The new algorithm is then evaluated with experiments on several common datasets and the results show that the proposed algorithm is more efficient compared to the original FCM as well as other clustering methods.https://www.mdpi.com/1999-4893/13/7/158clustering techniquefuzzy clusteringfuzzy C-means clusteringfuzzification coefficientobjective functionperformance indices |
| spellingShingle | Tran Dinh Khang Nguyen Duc Vuong Manh-Kien Tran Michael Fowler Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients Algorithms clustering technique fuzzy clustering fuzzy C-means clustering fuzzification coefficient objective function performance indices |
| title | Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients |
| title_full | Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients |
| title_fullStr | Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients |
| title_full_unstemmed | Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients |
| title_short | Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients |
| title_sort | fuzzy c means clustering algorithm with multiple fuzzification coefficients |
| topic | clustering technique fuzzy clustering fuzzy C-means clustering fuzzification coefficient objective function performance indices |
| url | https://www.mdpi.com/1999-4893/13/7/158 |
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