Knowledge discovery and semantic learning in the framework of axiomatic fuzzy set theory-Reference-Cited by-同舟云学术

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Knowledge discovery and semantic learning in the framework of axiomatic fuzzy set theory

Published:2018-06-26 Issue:5 Volume:8 Page:
ISSN:1942-4787
Container-title:WIREs Data Mining and Knowledge Discovery
language:en
Short-container-title:WIREs Data Mining Knowl Discov

Author:

Liu Xiaodong¹^ORCID,Jia Wenjuan¹,Wang Yuangang¹,Guo Hongyue²,Ren Yan³,Li Zedong⁴

Affiliation:

1. School of Control Science and Engineering, Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianChina

2. Collaborative Innovation Center for Transport StudiesDalian Maritime UniversityDalianChina

3. College of AutomationShenyang Aerospace UniversityShenyangChina

4. Dalian Key Lab of Digital Technology for National CultureDalian Minzu UniversityDalianChina

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

General Computer Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/widm.1268

Reference80 articles.

1. A Big Data Clustering Algorithm for Mitigating the Risk of Customer Churn

2. A Study of Notations and Illustrations of Axiomatic Fuzzy Set Theory

3. Monotone concepts for formal concept analysis

4. Ding R. Liu X. &Chen Y. (2006). The fuzzy clustering algorithm based on afs topology. InInternational Conference on Fuzzy Systems and Knowledge Discovery vol. 4223. Berlin and Heidelberg Germany: Springer; pp.89–98.

5. AFSNN: A Classification Algorithm Using Axiomatic Fuzzy Sets and Neural Networks

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