Single-Valued Neutrosophic Clustering Algorithms Based on Similarity Measures
Jun Ye ()
Additional contact information
Jun Ye: Shaoxing University
Journal of Classification, 2017, vol. 34, issue 1, No 8, 148-162
Abstract:
Abstract Clustering plays an important role in data mining, pattern recognition, and machine learning. Then, single-valued neutrosophic sets (SVNSs) can describe and handle indeterminate and inconsistent information, while fuzzy sets and intuitionistic fuzzy sets cannot describe and deal with it. To cluster the information represented by single-valued neutrosophic data, this paper proposes single-valued neutrosophic clustering algorithms based on similarity measures of SVNSs. Firstly, we introduce a similarity measure between SVNSs based on the min and max operators and propose another new similarity measure between SVNSs. Then, we present clustering algorithms based on the similarity measures of SVNSs for the clustering analysis of single-valued neutrosophic data. Finally, an illustrative example is given to demonstrate the application and effectiveness of the single-valued neutrosophic clustering algorithms.
Keywords: Single-valued neutrososophic set; Clustering algorithm; Similarity measure; Similarity matrix (search for similar items in EconPapers)
Date: 2017
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://link.springer.com/10.1007/s00357-017-9225-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:jclass:v:34:y:2017:i:1:d:10.1007_s00357-017-9225-y
Ordering information: This journal article can be ordered from
http://www.springer. ... hods/journal/357/PS2
DOI: 10.1007/s00357-017-9225-y
Access Statistics for this article
Journal of Classification is currently edited by Douglas Steinley
More articles in Journal of Classification from Springer, The Classification Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().