期刊
JOURNAL OF INTELLIGENT SYSTEMS
卷 23, 期 3, 页码 311-324出版社
WALTER DE GRUYTER GMBH
DOI: 10.1515/jisys-2013-0075
关键词
Neutrosophic set; single-valued neutrosophic set; minimum spanning tree; clustering algorithm; generalized distance measure
Clustering plays an important role in data mining, pattern recognition, and machine learning. Then, single-valued neutrosophic sets (SVNSs) are a useful means to describe and handle indeterminate and inconsistent information, which fuzzy sets and intuitionistic fuzzy sets cannot describe and deal with. To cluster the data represented by single-value neutrosophic information, the article proposes a single-valued neutrosophic minimum spanning tree (SVNMST) clustering algorithm. Firstly, we defined a generalized distance measure between SVNSs. Then, we present an SVNMST clustering algorithm for clustering single-value neutrosophic data based on the generalized distance measure of SVNSs. Finally, two illustrative examples are given to demonstrate the application and effectiveness of the developed approach.
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