Fuzzy clustering is an approach using the fuzzy set theory as a tool for data grouping, which has advantages over traditional clustering in many applications. Many fuzzy clustering algorithms have been developed in the literature including fuzzy c-means and possibilistic clustering algorithms, which are all objective-function based methods. Different from the existing fuzzy clustering approache...

The assessment and selection of green supplier development programs are an intriguing and functional research subject. This paper proposes a group decision-making approach considering possibilistic statistical concepts under uncertainty to assess green supplier development programs (GSDPs) via interval-valued fuzzy sets (IVFSs). Possibility theory is employed to regard uncertainty by IVFSs. A n...

Possibilistic decision theory has been proposed twenty years ago and has had several extensions since then. Because of the lack of decision power of possibilistic decision theory, several refinements have then been proposed. Unfortunately, these refinements do not allow to circumvent the difficulty when the decision problem is sequential. In this article, we propose to extend lexicographic refi...

This paper proposes a new decision making approch based on quantitative possibilistic influence diagrams which are extension of standard influence diagrams in the possibilistic framework. We will in particular treat the case where several expert opinions relative to value nodes are available. An initial expert assigns confidence degrees to other experts and fixes a similarity threshold that pro...

Developing efficient approaches for reasoning under inconsistency is an important issue in many applications. Several methods have been proposed to compile, possibly inconsistent, weighted or stratified bases. This paper focuses on the well-known linear order and possibilistic logic strategies. It provides a way for compiling a stratified belief base in order to be able to process inference fro...

Possibilistic decision theory has been proposed twenty years ago and has had several extensions since then. Because of the lack of decision power of possibilistic decision theory, several refinements have then been proposed. Unfortunately, these refinements do not allow to circumvent the difficulty when the decision problem is sequential. In this article, we propose to extend lexicographic refi...

Fuzzy data given by expert knowledge can be regarded as a possibility distribution by which possibilistic linear systems are defined. Recently, it has become important to deal with fuzzy data in connection with expert knowledge. Three formulations of possibilistic linear regression analysis are proposed here to deal with fuzzy data. Since our formulations can be reduced to linear programming pr...

Vast pools of historical financial information are available on economies, industry, and individual companies that affect investors’ selection of appropriate portfolios. Fuzzy data provides a good tool to reflect investors’ opinions based on this information. A possibilistic mean variance safety-first portfolio selection model is developed to support investors’ decision making, to take into con...