Journal
JOURNAL OF INTELLIGENT SYSTEMS
Volume 23, Issue 4, Pages 379-389Publisher
DE GRUYTER POLAND SP Z O O
DOI: 10.1515/jisys-2013-0091
Keywords
Neutrosophic set; single-valued neutrosophic set; clustering algorithm; distance measure; similarity measure
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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 cluster the data represented by single-valued neutrosophic information, this article proposes single-valued neutrosophic clustering methods based on similarity measures between SVNSs. First, we define a generalized distance measure between SVNSs and propose two distance-based similarity measures of SVNSs. Then, we present a clustering algorithm based on the similarity measures of SVNSs to cluster single-valued neutrosophic data. Finally, an illustrative example is given to demonstrate the application and effectiveness of the developed clustering methods.
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