文献类型：期刊文献

英文题名：Clustering Methods Using Distance-Based Similarity Measures of Single-Valued Neutrosophic Sets

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：4

起止页码：379

外文期刊名：JOURNAL OF INTELLIGENT SYSTEMS

收录：ESCI(收录号：WOS:000210736900002)、EI(收录号：20144400135375)、Scopus(收录号：2-s2.0-84908099940)、WOS

语种：英文

外文关键词：Neutrosophic set; single-valued neutrosophic set; clustering algorithm; distance measure; similarity measure

外文摘要：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.