Local Boundary Fuzzified Rough <i>K</i>-Means-Based Information Granulation Algorithm Under the Principle of Justifiable Granularity-Reference-Cited by-同舟云学术

Local Boundary Fuzzified Rough K-Means-Based Information Granulation Algorithm Under the Principle of Justifiable Granularity

Published:2024-01 Issue:1 Volume:54 Page:519-532
ISSN:2168-2267
Container-title:IEEE Transactions on Cybernetics
language:
Short-container-title:IEEE Trans. Cybern.

Author:

Zhang Tengfei¹^ORCID,Zhang Yudi¹^ORCID,Ma Fumin²^ORCID,Peng Chen³^ORCID,Yue Dong¹^ORCID,Pedrycz Witold⁴^ORCID

Affiliation:

1. College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, China

2. College of Information Engineering, Nanjing University of Finance and Economics, Nanjing, China

3. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China

4. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

Funder

National Natural Science Foundation of China

Key Research and Development Plan of Jiangsu Province, China

“Qing Lan” Project of Jiangsu Province, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Human-Computer Interaction,Information Systems,Control and Systems Engineering,Software

Link

http://xplorestaging.ieee.org/ielx7/6221036/10367425/10089155.pdf?arnumber=10089155

Reference42 articles.

1. Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic

2. Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems

3. From Numeric to Granular Models: A Quest for Error and Performance Analysis

4. Data quality measures based on granular computing for multi-label classification

5. A novel clustering ensemble model based on granular computing

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