文献类型：期刊文献

英文题名：Single-Valued Neutrosophic Minimum Spanning Tree and Its Clustering Method

作者：Ye, Jun[1]

机构：[1]Shaoxing Univ, Dept Elect & Informat Engn, 508 Huancheng West Rd, Shaoxing 312000, Zhejiang, Peoples R China

年份：2014

卷号：23

期号：3

起止页码：311

外文期刊名：JOURNAL OF INTELLIGENT SYSTEMS

收录：ESCI(收录号：WOS:000210735900006)、EI(收录号：20144200099877)、Scopus(收录号：2-s2.0-84907697921)、WOS

语种：英文

外文关键词：Neutrosophic set; single-valued neutrosophic set; minimum spanning tree; clustering algorithm; generalized distance measure

外文摘要：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.