文献类型：期刊文献

英文题名：Single-Valued Neutrosophic Clustering Algorithms Based on Similarity Measures

作者：Ye, Jun[1]

机构：[1]Shaoxing Univ, Shaoxing, Zhejiang, Peoples R China

年份：2017

卷号：34

期号：1

起止页码：148

外文期刊名：JOURNAL OF CLASSIFICATION

收录：SSCI(收录号：WOS:000399173700008)、SCI-EXPANDED(收录号：WOS:000399173700008)、、Scopus(收录号：2-s2.0-85017111293)、WOS

语种：英文

外文关键词：Single-valued neutrososophic set; Clustering algorithm; Similarity measure; Similarity matrix

外文摘要：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.