Clustering Methods Using Distance-Based Similarity Measures of Single-Valued Neutrosophic Sets

Clustering Methods Using Distance-Based Similarity Measures of Single-Valued Neutrosophic Sets

Clustering plays an important role in data mining, pattern recognition, and machine learning. Single-valued neutrosophic sets (SVNSs) are useful means to describe and handle indeterminate and inconsistent information that fuzzy sets and intuitionistic fuzzy sets cannot describe and deal with. To clu...

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Bibliographic Details
Main Author: Ye Jun
Format: Article
Language:English
Published: De Gruyter 2014-12-01
Series:Journal of Intelligent Systems
Subjects:
neutrosophic set
single-valued neutrosophic set
clustering algorithm
distance measure
similarity measure
Online Access:https://doi.org/10.1515/jisys-2013-0091

Internet

https://doi.org/10.1515/jisys-2013-0091

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