This chapter tries to answer the fundamental question of what main contributions of fuzzy clustering to the theory of cluster analysis from theoretical viewpoints. While fuzzy clustering is thought to be clearly useful by users of this technique, others think that the concept of fuzziness is not needed in clustering. Thus the usefulness of fuzzy clustering is not trivial. The discussion here is...

The fuzzy information retrieval model was proposed some years ago to solve several limitations of the Boolean model without a need of a complete redesign of the information retrieval system. However, the complexity of the fuzzy query language makes it difficult to formulate user queries. Among other proposed approaches to solve this problem, we find the Inductive Query by Example (IQBE) framewo...

The success of machine learning methods for inducing models from data crucially depends on the proper incorporation of background knowledge about the model to be learned. The idea of constraint-regularized learning is to employ fuzzy set-based modeling techniques in order to express such knowledge in a flexible way, and to formalize it in terms of fuzzy constraints. Thus, background knowledge c...

In this paper, we propose accurate linguistic modeling, a methodology to design linguistic models that are accurate to a high degree and may be suitably interpreted. This approach will be based on two main assumptions related to the interpolative reasoning developed by fuzzy rule-based systems: a small change in the structure of the linguistic model based on allowing the linguistic rule to have...

The aim of this work is to present a comparative study of the wages in Spain from the year 1967 to the year 1988. Leaving from a classic model, as it is the MOISSES model (econometric model of the representative series of the Spanish macroeconomics), we obtain two fuzzy models by using these sources of data. Considering the domain of the MOISSES model, and by using an inductive mechanism of hie...

In the design of an interpretable fuzzy rule-based classification system (FRBCS) the precision as much as the simplicity of the extracted knowledge must be considered as objectives. In any inductive learning algorithm, when we deal with problems with a large number of features, the exponential growth of the fuzzy rule search space makes the learning process more difficult. Moreover it leads to ...

This paper presents a neuro-fuzzy internal model Cartesian controller for robot manipulators. An inductive learning technique is applied to generate the required inverse dynamics and inverse kinematics modelling rules from input/output measurements. A fully differentiable fuzzy neural network is used to construct the adaptive sections of the controller for on-line parameters adaptation. A fuzzy...

ABsTRAcT This chapter describes how to integrate medical knowledge with purely inductive (data-driven) methods for the creation of clinical prediction rules. It addresses three issues: representation of medical knowledge , secondary analysis of medical data, and evaluation of automatically induced predictive models in the context of existing knowledge. To address the complexity of the domain kn...

The fuzzy K-means problem is a generalization of the classical K-means problem to soft clusterings, i.e. clusterings where each points belongs to each cluster to some degree. Although popular in practice, prior to this work the fuzzy K-means problem has not been studied from a complexity theoretic or algorithmic perspective. We show that optimal solutions for fuzzy K-means cannot, in general, b...

We have calculated interactions between two fuzzy spheres in 3 dimension. It depends on the distance r between the spheres and the radii ρ1, ρ2. There is no force between the spheres when they are far from each other (long distance case). We have also studied the interaction for r = 0 case. We find that an attractive force exists between two fuzzy sphere surfaces.