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- W2950033410 abstract "Clustering plays an important role in data mining, pattern recognition, and machine learning. Singlevalued 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." @default.
- W2950033410 created "2019-06-27" @default.
- W2950033410 creator A5036728386 @default.
- W2950033410 date "2014-11-01" @default.
- W2950033410 modified "2023-09-27" @default.
- W2950033410 title "Clustering Methods Using Distance-Based Similarity Measures of Single-Valued Neutrosophic Sets" @default.
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- W2950033410 hasPublicationYear "2014" @default.
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