Clustering Methods Using Distance-Based Similarity Measures of Single-Valued Neutrosophic Sets

Journal of Intelligent Systems

卷:23

Ye Jun¹ 

[1] Department of Electrical and Information Engineering, Shaoxing University, 508 Huancheng West Road, Shaoxing, Zhejiang Province 312000, P.R. China;

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

DOI  :  10.1515/jisys-2013-0091

来源: DOAJ

【 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.

【 授权许可】

Unknown