Paper Info
Title
Knowledge discovery and semantic learning in the framework of axiomatic fuzzy set theory.
Abstract
Axiomatic fuzzy set (AFS) theory facilitates a way on how to transform data into fuzzy sets (membership functions) and implement their fuzzy logic operations, which provides a flexible and powerful tool for representing human knowledge and emulate human recognition process. In recent years, AFS theory has received increasing interest. In this survey, we report the current developments of theoretical research and practical advances in the AFS theory. We first review some notion and foundations of the theory with an illustrative example, then, we focus on the various extensions of AFS theory for knowledge discovery, including clustering, classification, rough sets, formal concept analysis, and other learning tasks. Due to its unique characteristics of semantic representation, AFS theory has been applied in multiple domains, such as business intelligence, computer vision, financial analysis, and clinical data analysis. This survey provides a comprehensive view of these advances in AFS theory and its potential perspectives. This article is categorized under: Technologies > Computational Intelligence
Year
DOI
Venue
2018
10.1002/widm.1268
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY
Keywords
Field
DocType
axiomatic fuzzy sets,data mining,knowledge discovery,pattern recognition,semantic representation
Axiom,Computer science,Fuzzy set,Semantic learning,Knowledge extraction,Artificial intelligence,Natural language processing,Semantic representation,Machine learning
Journal
Volume
Issue
ISSN
8.0
5.0
1942-4787
Citations 
PageRank 
References 
3
0.38
30
Authors
6
Name
Order
Citations
PageRank
Xiaodong Liu149228.50
Wenjuan Jia241.40
Yuangang Wang3284.50
Hongyue Guo430.38
Yan Ren5719.07
Zedong Li6223.39