Open AccessSingle Valued Neutrosophic Similarity Measures for Multiple Attribute Decision-Making
Author(s) -
Jun Ye,
Qiansheng Zhang
Publication year - 2014
Publication title -
zenodo (cern european organization for nuclear research)
Language(s) - English
DOI - 10.5281/zenodo.571756
Subject(s) - similarity (geometry) , computer science , artificial intelligence , mathematics , data mining , image (mathematics)
Similarity measures play an important role in data mining, pattern recognition, decision making, machine learning, image process etc. Then, single valued neutrosophic sets (SVNSs) can describe and handle the indeterminate and inconsistent information, which fuzzy sets and intuitionistic fuzzy sets cannot describe and deal with. Therefore, the paper proposes new similarity meas-ures between SVNSs based on the minimum and maxi-mum operators