Semi-supervised fuzzy clustering algorithm based on prior membership degree matrix with expert preference
Existing pre-processing methods for the prior membership degree matrix suffer from the following issues: (1) The labeling constraints for prior membership degree matrix have an effect on the expert’s judgment on the prior membership degree, which easily causes the distortion problem of the prior mem...
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| Veröffentlicht in: | Expert systems with applications Jg. 238; S. 121812 |
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| Sprache: | Englisch |
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15.03.2024
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | Existing pre-processing methods for the prior membership degree matrix suffer from the following issues: (1) The labeling constraints for prior membership degree matrix have an effect on the expert’s judgment on the prior membership degree, which easily causes the distortion problem of the prior membership degree labeling information; (2) There exists the problem of inconsistency between the filling information and the labeling information in the prior membership degree matrix to be filled in the missing values with zeros. To address these problems, we propose an unconstrained labeling idea for the prior membership degree matrix and the corresponding pre-processing method for the missing values by introducing the statistical characteristics of extreme value distribution and simultaneously apply it to the semi-supervised fuzzy clustering algorithm. More specifically, we focus on learning an expert preference value from the prior membership degree matrix and filling in the missing values with the expert preference value. Thus, we propose an unconstrained pre-processing method for the prior membership degree matrix by filling in missing values with an expert preference to keep the filling information consistent with the labeling information in the prior membership degree matrix as much as possible. In addition, we design a semi-supervised fuzzy clustering algorithm based on an unconstrained prior membership degree matrix with expert preference (SFCM-EP) by introducing the K-L divergence to improve the applicability, utility and running performance of semi-supervised fuzzy clustering algorithm. Our experimental results on the simulation dataset and the UCI datasets show the feasibility and effectiveness of the proposed pre-processing method of the prior membership degree matrix with encouraging results. |
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| AbstractList | Existing pre-processing methods for the prior membership degree matrix suffer from the following issues: (1) The labeling constraints for prior membership degree matrix have an effect on the expert’s judgment on the prior membership degree, which easily causes the distortion problem of the prior membership degree labeling information; (2) There exists the problem of inconsistency between the filling information and the labeling information in the prior membership degree matrix to be filled in the missing values with zeros. To address these problems, we propose an unconstrained labeling idea for the prior membership degree matrix and the corresponding pre-processing method for the missing values by introducing the statistical characteristics of extreme value distribution and simultaneously apply it to the semi-supervised fuzzy clustering algorithm. More specifically, we focus on learning an expert preference value from the prior membership degree matrix and filling in the missing values with the expert preference value. Thus, we propose an unconstrained pre-processing method for the prior membership degree matrix by filling in missing values with an expert preference to keep the filling information consistent with the labeling information in the prior membership degree matrix as much as possible. In addition, we design a semi-supervised fuzzy clustering algorithm based on an unconstrained prior membership degree matrix with expert preference (SFCM-EP) by introducing the K-L divergence to improve the applicability, utility and running performance of semi-supervised fuzzy clustering algorithm. Our experimental results on the simulation dataset and the UCI datasets show the feasibility and effectiveness of the proposed pre-processing method of the prior membership degree matrix with encouraging results. |
| ArticleNumber | 121812 |
| Author | Zhu, Yuanhao Liu, Bo Xiao, Yunhao Xu, Shengbing Wang, Zhenyou Hao, Zhifeng |
| Author_xml | – sequence: 1 givenname: Shengbing orcidid: 0000-0002-3914-6555 surname: Xu fullname: Xu, Shengbing email: xushengbing@gdut.edu.cn organization: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, Guangdong 51006, P.R. China – sequence: 2 givenname: Zhifeng surname: Hao fullname: Hao, Zhifeng organization: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, Guangdong 51006, P.R. China – sequence: 3 givenname: Yuanhao orcidid: 0000-0002-4135-6113 surname: Zhu fullname: Zhu, Yuanhao organization: School of Mathematics and Statistics, Guangdong University of Technology, Guangzhou, Guangdong 510520, P.R. China – sequence: 4 givenname: Zhenyou orcidid: 0000-0002-5483-1681 surname: Wang fullname: Wang, Zhenyou organization: School of Mathematics and Statistics, Guangdong University of Technology, Guangzhou, Guangdong 510520, P.R. China – sequence: 5 givenname: Yunhao orcidid: 0000-0001-8918-1984 surname: Xiao fullname: Xiao, Yunhao organization: School of Mathematics and Statistics, Guangdong University of Technology, Guangzhou, Guangdong 510520, P.R. China – sequence: 6 givenname: Bo surname: Liu fullname: Liu, Bo organization: School of Automation, Guangdong University of Technology, Guangzhou, Guangdong 510006, P.R. China |
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| Keywords | Fuzzy clustering Constraints Missing value Semi-supervised clustering Prior membership degree |
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