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Occluded offline handwritten Chinese character inpainting via generative adversarial network and self-attention mechanism. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.07.046] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Occluded offline handwritten Chinese character recognition using deep convolutional generative adversarial network and improved GoogLeNet. Neural Comput Appl 2020. [DOI: 10.1007/s00521-018-3854-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Lin M, Wang H, Xu Z, Yao Z, Huang J. Clustering algorithms based on correlation coefficients for probabilistic linguistic term sets. INT J INTELL SYST 2018. [DOI: 10.1002/int.22040] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Mingwei Lin
- College of Mathematics and Informatics, Fujian Normal University; Fuzhou China
| | - Huibing Wang
- College of Mathematics and Informatics, Fujian Normal University; Fuzhou China
| | - Zeshui Xu
- Business School, Sichuan University; Chengdu China
| | - Zhiqiang Yao
- College of Mathematics and Informatics, Fujian Normal University; Fuzhou China
| | - Jinli Huang
- College of Mathematics and Informatics, Fujian Normal University; Fuzhou China
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Abstract
Fuzzy clustering is a widely used approach for data classification by using the fuzzy set theory. The probability measure and the possibility measure are two popular measures which have been used in the fuzzy [Formula: see text]-means algorithm (FCM) and the possibilistic clustering algorithms (PCAs), respectively. However, the numerical experiments revealed that FCM and its derivatives lack the intuitive concept of degree of belongingness, and PCAs suffer from the “coincident problem” and cannot provide very stable results for some data sets. In this study, we propose a new clustering algorithm, called the credibilistic clustering algorithm (CCA), based on the credibility measure. The credibility measure provides some unique properties which can solve the “coincident problem” and noise issue compared with the probability measure and possibility measure. Based on some randomly generated data sets, experimental results compared with FCM and PCA show that CCA can deal with the “coincident problem” with good clustering results, and it is more robust to noise than PCA.
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Affiliation(s)
- Jian Zhou
- School of Management, Shanghai University, Shanghai 200444, China
| | - Qina Wang
- School of Management, Shanghai University, Shanghai 200444, China
| | - C.-C. Hung
- Anyang Normal University, Anyang 455000, China
- School of Computing and Software Engineering, Southern Polytechnic State University, Marietta, GA 30060, USA
| | - Xiajie Yi
- School of Management, Shanghai University, Shanghai 200444, China
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