We develop a three-step fuzzy logic-based algorithm for clustering categorical attributes, and we apply it to analyze cultural data. In the first step the algorithm employs an entropy-based clustering scheme, which initializes the cluster centers. In the second step we apply the fuzzy c-modes algorithm to obtain a fuzzy partition of the data set, and the third step introduces a novel cluster va...

Fuzzy rule interpolation based reasoning methods are the most common choices for cases when the applied rule base is not dense. This paper presents a new technique called LESFRI, which is based on the method of least squares. Its central idea is the conservation of the shape type specific to a fuzzy partition. The method has low computational complexity.

Fuzzy transform is a powerful tool for approximation of continuous functions. The paper aims at constructing approximation models on the basis of a generalization of fuzzy partition. We will prove the best approximation properties in the corresponding approximation spaces. Mathematics Subject Classificaion: 41A30, 41A45

An approach to data-driven linguistic modeling is presented. The methodology is based on a fuzzy system with relational input partition that allows for transparent modeling of linear dependencies between the inputs. An identification algorithm for this type of fuzzy system is proposed. It automatically finds strongest dependencies from numerical data. An application example illustrates the usef...

Fuzzy grid partition has been used to produce appropriate and optimal output. The output of several fuzzy inference methods such as the Tsukamoto method Mamdani have improved by applying partitions. This study aims apply partition-based mamdani determine feasibility increasing employee salaries. stages are carried out starting with determining number partitions, forming sets, carrying process i...

This paper presents a multistage random sampling fuzzy c-means based clustering algorithm, which signi cantly reduces the computation time required to partition a data set into c classes. A series of subsets of the full data set are used for classi cation in order to provide an approximation to the nal cluster centers. The quality of the nal partitions is equivalent to that of fuzzy c-means. Th...

This paper proposes a general discussion of the handling of imprecise and uncertain information in temporal reasoning in the framework of fuzzy sets and possibility theory. The introduction of fuzzy features in temporal reasoning can be related to different issues. First, it can be motivated by the need of a gradual, linguistic-like description of temporal relations even in the face of complete...

Reinforcement Learning (RL) is a widely used learning paradigm for adaptive agents. Because exact RL can only be applied to very simple problems, approximate algorithms are usually necessary in practice. Many algorithms for approximate RL rely on basis-function representations of the value function (or of the Q-function). Designing a good set of basis functions without any prior knowledge of th...

This paper presents an approach to building multi-input and single-output fuzzy models. Such a model is composed of fuzzy implications, and its output is inferred by simplified reasoning. The implications are automatically generated by the structure and parameter identification. In structure identification, the optimal or near optimal number of fuzzy implications is determined in view of valid ...

Fuzzy association rules described by the natural language are well suited for the thinking of human subjects and will help to increase the flexibility for supporting users in making decisions or designing the fuzzy systems. In this paper, a new algorithm named fuzzy grids based rules mining algorithm (FGBRMA) is proposed to generate fuzzy association rules from a relational database. The propos...