Entropy is commonly used as a way to measure the uncertainty of random variables. In uncertain set theory, a concept of entropy for uncertain sets has been defined by using logarithm. However, such an entropy fails to measure the uncertain degree of some uncertain sets. This paper aims at proposing a concept of elliptic entropy for uncertain sets, and investigating its properties such as transl...

In this paper, we improve the fuzzy compromise approach of Guu and Wu by automatically computing proper membership thresholds instead of choosing them. Indeed, in practice, choosing membership thresholds arbitrarily may result in an infeasible optimization problem. Although we can adjust minimum satisfaction degree to get fuzzy efficient solution, it sometimes makes the process of interactionmo...

Abstract: In recent years, neutrosophic sets (NSs) have attracted widespread attentions and been widely applied to multiple attribute decision-making (MADM). The interval neutrosophic set (INS) is an extension of NS, in which the truth-membership, indeterminacy-membership and falsity-membership degree are expressed by interval values, respectively. Obviously, INS can conveniently describe compl...

This paper addresses the development of organizational multi agent systems as a preferred solution to develop open, distributed and adaptive application. It proposes a combination between components and agents to define a flexible organizational model of MAS based on three concepts: roles, self-adaptive agents based on components and fuzzy groups. Roles are played by agents in fuzzy groups. A f...

Identification of grammars (r. e. indices) for recursively enumerable languages from positive data by algorithmic devices is a well studied problem in learning theory. The present paper considers identification of r. e. languages by machines that have access to membership oracles for noncomputable sets. It is shown that for any set A there exists another set B such that the collections of r. e....

Many clustering methods have been proposed, including fuzzy k-means, which allows an object to be assigned to multi-clusters with different degree of membership. However, the memberships that result from fuzzy k-means, are rarely analyzed and visualized properly, but converted to 0-1 memberships. In this paper, we propose a new approach to visualize fuzzy-clustered data. The scheme provides a g...

its membership – one of them contributed to the withdrawal of the arthritis drug Vioxx. With this track record, and the option to bank on some degree of community spirit among its membership, Kaiser Permanente may find it easier to gain the trust of the target population than the UK Biobank did. They also may want to find a different name for the project, for who would trust a bank these days? ...

In this study, we formulated and tested a theory about how heterogeneity among subpopulations affects the degree to which organizations in these subpopulations contribute to and benefit from the overall population’s legitimacy. Historical data on banking in Shanghai were used. Firms in subpopulations with a higher “grade of membership” were found to contribute strongly to the legitimacy of the ...

Clustering involves the partitioning of n objects into k clusters. Many clustering algorithms use hard-partitioning techniques where each object is assigned to one cluster. In this paper we propose an overlapping algorithm MCOKE which allows objects to belong to one or more clusters. The algorithm is different from fuzzy clustering techniques because objects that overlap are assigned a membersh...