Local search genetic algorithm-based possibilistic weighted fuzzy c-means for clustering mixed numerical and categorical data
Clustering for mixed numerical and categorical attributes has attracted many researchers due to its necessity in many real-world applications. One crucial issue concerned in clustering mixed data is to select an appropriate distance metric for each attribute type. Besides, some current clustering me...
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| Vydané v: | Neural computing & applications Ročník 34; číslo 20; s. 18059 - 18074 |
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| Hlavní autori: | , , , , |
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| Jazyk: | English |
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Springer London
01.10.2022
Springer Nature B.V |
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| ISSN: | 0941-0643, 1433-3058 |
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| Abstract | Clustering for mixed numerical and categorical attributes has attracted many researchers due to its necessity in many real-world applications. One crucial issue concerned in clustering mixed data is to select an appropriate distance metric for each attribute type. Besides, some current clustering methods are sensitive to the initial solutions and easily trap into a locally optimal solution. Thus, this study proposes a local search genetic algorithm-based possibilistic weighted fuzzy
c
-means (LSGA-PWFCM) for clustering mixed numerical and categorical data. The possibilistic weighted fuzzy c-means (PWFCM) is firstly proposed in which the object-cluster similarity measure is employed to calculate the distance between two mixed-attribute objects. Besides, each attribute is placed a different important role by calculating its corresponding weight in the PWFCM procedure. Thereafter, GA is used to find a set of optimal parameters and the initial clustering centroids for the PFCM algorithm. To avoid local optimal solution, local search-based variable neighborhoods are embedded in the GA procedure. The proposed LSGA-PWFCM algorithm is compared with other benchmark algorithms based on some public datasets in UCI machine learning repository to evaluate its performance. Two clustering validation indices are used, i.e., clustering accuracy and Rand index. The experimental results show that the proposed LSGA-PWFCM outperforms other algorithms on most of the tested datasets. |
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| AbstractList | Clustering for mixed numerical and categorical attributes has attracted many researchers due to its necessity in many real-world applications. One crucial issue concerned in clustering mixed data is to select an appropriate distance metric for each attribute type. Besides, some current clustering methods are sensitive to the initial solutions and easily trap into a locally optimal solution. Thus, this study proposes a local search genetic algorithm-based possibilistic weighted fuzzy c-means (LSGA-PWFCM) for clustering mixed numerical and categorical data. The possibilistic weighted fuzzy c-means (PWFCM) is firstly proposed in which the object-cluster similarity measure is employed to calculate the distance between two mixed-attribute objects. Besides, each attribute is placed a different important role by calculating its corresponding weight in the PWFCM procedure. Thereafter, GA is used to find a set of optimal parameters and the initial clustering centroids for the PFCM algorithm. To avoid local optimal solution, local search-based variable neighborhoods are embedded in the GA procedure. The proposed LSGA-PWFCM algorithm is compared with other benchmark algorithms based on some public datasets in UCI machine learning repository to evaluate its performance. Two clustering validation indices are used, i.e., clustering accuracy and Rand index. The experimental results show that the proposed LSGA-PWFCM outperforms other algorithms on most of the tested datasets. Clustering for mixed numerical and categorical attributes has attracted many researchers due to its necessity in many real-world applications. One crucial issue concerned in clustering mixed data is to select an appropriate distance metric for each attribute type. Besides, some current clustering methods are sensitive to the initial solutions and easily trap into a locally optimal solution. Thus, this study proposes a local search genetic algorithm-based possibilistic weighted fuzzy c -means (LSGA-PWFCM) for clustering mixed numerical and categorical data. The possibilistic weighted fuzzy c-means (PWFCM) is firstly proposed in which the object-cluster similarity measure is employed to calculate the distance between two mixed-attribute objects. Besides, each attribute is placed a different important role by calculating its corresponding weight in the PWFCM procedure. Thereafter, GA is used to find a set of optimal parameters and the initial clustering centroids for the PFCM algorithm. To avoid local optimal solution, local search-based variable neighborhoods are embedded in the GA procedure. The proposed LSGA-PWFCM algorithm is compared with other benchmark algorithms based on some public datasets in UCI machine learning repository to evaluate its performance. Two clustering validation indices are used, i.e., clustering accuracy and Rand index. The experimental results show that the proposed LSGA-PWFCM outperforms other algorithms on most of the tested datasets. |
| Author | Nguyen, Thi Cuc Nguyen, Thi Phuong Quyen Kuo, R. J. Le, Minh Duc Le, Thi Huynh Anh |
| Author_xml | – sequence: 1 givenname: Thi Phuong Quyen orcidid: 0000-0002-8559-8050 surname: Nguyen fullname: Nguyen, Thi Phuong Quyen email: ntpquyen@dut.udn.vn organization: Faculty of Project Management, The University of Danang–University of Science and Technology – sequence: 2 givenname: R. J. surname: Kuo fullname: Kuo, R. J. organization: Department of Industrial Management, National Taiwan University of Science and Technology – sequence: 3 givenname: Minh Duc surname: Le fullname: Le, Minh Duc organization: Faculty of Transportation Mechanical Engineering, The University of Danang–University of Science and Technology – sequence: 4 givenname: Thi Cuc surname: Nguyen fullname: Nguyen, Thi Cuc organization: Faculty of Project Management, The University of Danang–University of Science and Technology – sequence: 5 givenname: Thi Huynh Anh surname: Le fullname: Le, Thi Huynh Anh organization: Faculty of Project Management, The University of Danang–University of Science and Technology |
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| Keywords | Local search genetic algorithm Possibilistic fuzzy means Variable neighborhood search Mixed data |
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