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A study on multi-kernel intuitionistic fuzzy C-means clustering with multiple attributes

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成果类型:
期刊论文
作者:
Zeng, Shan;Wang, Zhiyong;Huang, Rui*;Chen, Ling;Feng, David
通讯作者:
Huang, Rui
作者机构:
[Zeng, Shan; Chen, Ling] Wuhan Polytech Univ, Coll Math & Comp Sci, Wuhan 430023, Hubei, Peoples R China.
[Feng, David; Wang, Zhiyong] Univ Sydney, Sch Informat Technol, Sydney, NSW 2006, Australia.
[Feng, David; Wang, Zhiyong] Univ Sydney, BMIT Res Grp, Sydney, NSW 2006, Australia.
[Huang, Rui] Chinese Univ Hong Kong, Sch Sci & Engn, Shenzhen 518172, Guangdong, Peoples R China.
[Feng, David] Shanghai Jiao Tong Univ, Med X Res Inst, Shanghai 200240, Peoples R China.
通讯机构:
[Huang, Rui] C
Chinese Univ Hong Kong, Sch Sci & Engn, Shenzhen 518172, Guangdong, Peoples R China.
语种:
英文
关键词:
Fuzzy C-means clustering;Intuitionistic fuzzy sets;Multi-kernel model
期刊:
Neurocomputing
ISSN:
0925-2312
年:
2019
卷:
335
页码:
59-71
基金类别:
NSFC-CAAC [U1833119]; Hubei natural science foundationNatural Science Foundation of Hubei Province [2017CFB500]; Wuhan science and technology foundation [2018020401011299]; Robotic Discipline Development Fund from Shenzhen Gov, China [2016-1418]
机构署名:
本校为第一机构
院系归属:
数学与计算机学院
摘要:
Fuzzy C-means (FCM) clustering has been widely applied in various data-driven applications. While traditional FCM clustering algorithms handle uncertainty with type-2 Fuzzy Sets (T2 FSs), the recently-proposed intuitionistic fuzzy sets (IFSs) have shown advantages for describing vague and uncertain data by taking both membership degree and non-membership degree into account. However, intuitionistic fuzzy C-means (IFCM) algorithms generally do not take the importance of individual attributes and the structure of the data into account, when multi-modal and imbalanced features are involved in an ...

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