A Heuristic Algorithm of Possibilistic Clustering with Partial Supervision for Classification of the Intuitionistic Fuzzy Data
Jan W. Owsiński, Janusz Kacprzyk, Stanislau Shyrai, Eulalia Szmidt, Dmitri A. Viattchenin and Jorge Hernandez Hormazabal
The paper deals with the problem of clustering of intuitionistic fuzzy data. A modification of a heuristic algorithm of possibilistic clustering for intuitionistic fuzzy data that account for the information coming from the labeled objects is proposed. The paper describes the basic ideas of the method and gives the plan of the partially supervised version of a direct possibilistic clustering algorithm. Illustrative examples of application of the method to two intuitionistic fuzzy data sets are provided. Preliminary conclusions are formulated and some perspectives outlined, notably for the analysis of agricultural value chain.
Keywords: Intuitionistic fuzzy set, intuitionistic fuzzy tolerance, clustering, allotment among intuitionistic fuzzy clusters, membership degree, nonmembership degree, labeled object, partial supervision, agricultural value chain