A generalized approach to possibilistic clustering algorithms was proposed in [19], where the memberships are evaluated directly according to the data information using the fuzzy set theory, and the cluster centers are updated via a performance index. The computational experiments based on the generalized possibilistic clustering algorithms in [19] revealed that these clustering algorithms coul...

Multi-objective De Novo linear programming (MODNLP) is problem for designing optimal system by reshaping the feasible set (Fiala [3] ). This paper deals with MODNLP having possibilistic objective functions coefficients. The problem is considered by inserting possibilistic data in the objective functions coefficients. The solution of the problem is defined and established under the using of effi...

This paper uses a constraint set approach to linear programming problems with equality constraints whose coefficients and/or right-hand side values could be uncertain. We consider three types of uncertainty: probabilistic, fuzzy/possibilistic, and interval. The constraint set generated by the linear constraints under uncertainty is illdefined and difficult to generate. Our approach computes an ...

Up to now, several algorithms for clustering large data sets have been presented. Most clustering approaches for data sets are the crisp ones, which cannot be well suitable to the fuzzy case. In this paper, the authors explore a single pass approach to fuzzy possibilistic clustering over large data set. The basic idea of the proposed approach (weighted fuzzy-possibilistic c-means, WFPCM) is to ...

The current models and methods for PLP are usually restricted on some special types and usually the same type of possibilistic distribution. This paper focuses on linear programming problems with general possibilistic resources (GRPLP) and linear programming problems with general possibilistic objective coe cients (GOPLP). By introducing some new concepts of the largest most possible point, the...

Possibilistic linear equality systems (PLES) are linear equality systems with fuzzy parameters and crisp variables. We study the problem: For a given PLES with weakly noninteractive fuzzy number parameters how does the solution change if the parameters are varied?

We prove that possibilistic linear programming problems (introduced by Buckley in [2]) are well-posed, i.e. small changes of the membership function of the parameters may cause only a small deviation in the possibilistic distribution of the objective function.

Based on our earlier work on partial logics and extended logic programs Wag91, Wag94, HJW96], and on the possibilistic logic of DLP94], we deene a compositional possibilistic rst-order logic with two kinds of negation as the logical basis of semi-possibilistic and possibilis-tic logic programs. We show that in the same way as the minimal model semantics of relational databases can be reened to ...

a theorem was recently introduced to establish a relationship betweengoal programming and fuzzy programming for vectormaximum problems.in this short note it is shown that the relationship does not exist underall circumstances. the necessary correction is proposed.