Abstract
The note deals with the problem of heuristic possibilistic clustering the intuitionistic fuzzy data. Different distances between intuitionistic fuzzy sets are considered in the paper. Similarity measures for intuitionistic fuzzy sets for constructing intuitionistic fuzzy tolerance relations are also considered. A numerical example of application of these distances and similarity measures for clustering the intuitionistic fuzzy data is presented. Some preliminary conclusions are formulated.
Highlights
Cluster analysis is a group of approaches for classifying objects according to their likeness by means of unsupervised training
A principal allotment among fuzzy clusters is the result of application of the conventional D-PAFC-algorithm of the heuristic approach to possibilistic clustering to classification the attributive intuitionistic fuzzy data by using distances between intuitionistic fuzzy sets
A principal allotment among intuitionistic fuzzy clusters is the result of application of the D-PAIFC-algorithm to classification the data which can be obtained by using similarity measures
Summary
Cluster analysis is a group of approaches for classifying objects according to their likeness by means of unsupervised training. The group of direct relational heuristic algorithms of possibilistic clustering the intuitionistic fuzzy data contains a) the D-PAIFC-algorithm which is based on the construction of an principal allotment IRP∗ ( X ) among a priori unknown minimal number at least c fully separate intuitionistic fuzzy (α , β ) -clusters [7]; b) the D-AIFC(c)-algorithm which is based on the construction of an allotment IRc∗ ( X ) among a priori given number c of partially separate intuitionistic fuzzy (α , β ) -clusters [10]. The contents of this paper are the following: in the second section some definitions of the intuitionistic fuzzy set theory are described, in the third section distances between intuitionistic fuzzy sets are presented, in the fourth section similarity measures for constructing intuitionistic fuzzy tolerance relation are described, in the fifth section results of numerical experiments are presented, in sixth section some preliminary conclusions are formulated and perspectives of future investigations are outlined
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Sustainability Management and Information Technologies
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
