This paper studies the extension of possibilistic logic to the case when weights attached to formulas are symbolic and stand for variables that lie in a totally ordered scale, and only partial knowledge is available on the relative strength of these weights. A proof of the soundness and the completeness of this logic according to the relative certainty semantics in the sense of necessity measur...

This article discusses the basic features of information provided in terms of possibilistic uncertainty. It points out the entailment principle, a tool that allows one to infer less specific from a given piece of information. The problem of fusing multiple pieces of possibilistic information is and the basic features of probabilistic information are described. The authors detail a procedure for...

Possibilistic logic has been proposed as a numerical formalism for reasoning with uncertainty. There has been interest in developing qualitative accounts of possibility, as well as an explanation of the relationship between possibility and modal logics. We present two modal logics that can be used to represent and reason with qualitative statements of possibility and necessity. These logics hav...

A category of learning problems is considered, in which the class membership of training patterns is assessed by an expert and encoded in the form of a possibility distribution. Each example i thus consists in a feature vector xi and a possibilistic label (u1, . . . , u i c), where uk denotes the possibility of that example belonging to class k. This problem is tackled in the framework of Evide...

This paper deals in a preliminary way with the problem of clustering of three-way data. A method of the problem solving is based on the application of a direct possibilistic clustering algorithm based on the concept of allotment among fuzzy cluster to a matrix of feeble fuzzy tolerance, which represent a structure of the set of objects under uncertainty. The paper provides the description of ba...

Data mining technology has emerged as a means for identifying patterns and trends from large quantities of data. Data mining is a computational intelligence discipline that contributes tools for data analysis, discovery of new knowledge, and autonomous decision making. Clustering is a primary data description method in data mining which group’s most similar data. The data clustering is an impor...

The conventional wisdom is that the concept of information is closely related to the concept of probability. In Shannon's information theory, information is equated to a reduction in entropy—a probabilistic concept. In this paper, a different view of information is put on the table. Information is equated to restriction. More concretely, a restriction is a limitation on the values which a varia...

Central in standard possibilistic logic (where propositional logic formulas are associated with lower bounds of their necessity measures), is the notion of inconsistency level of a possibilistic logic base. Formulas whose level is strictly above this inconsistency level constitute a sub-base free of any inconsistency. Some extensions, based on the notions of paraconsistent completion of a possi...