This paper comprises two parts, the first deals with the conception of a class of Hybrid Granular Min-Max Fuzzy-Neuro Relational Learners, for which a learning scheme was devised that uses an exhaustive search over the fuzzy partitions of involved variables, automatic fuzzy hypotheses generation, formulation and testing, and successive approximation procedure of Min-Max relational equations. Th...

A fuzzy partition assigns to each among n objects a distribution over a categories. Elementary linear algebraic methods permit to introduce and investigate concepts and properties such as a) variance and inertia decomposition; b) coarseand fine-graining (nestedness); c) iteration of fuzzy partitions; d) stability of a group in regard to another partition; e) (euclidean embeddable) dissimilariti...

This paper deals with a real world problem of medical diagnosis, to this goal, we propose to learn a compact fuzzy medical knowledge base through a cognitively-motivated granular hybrid neuro-fuzzy or fuzzy-neuro possibilistic model appropriately crafted as a means to automatically extract fuzzy weighted production rules. The main idea is to start learning from coarse fuzzy partitions of the in...

Rand index is one of the most popular measures for comparing two partitions over a set of objects. Several approaches have extended this measure for those cases involving fuzzy partitions. In previous works, we developed a methodology for correspondence analysis between partitions in terms of data mining tools. In this paper we discuss how, without any additional cost, it can be applied as an a...

A neural network can approximate a function, but it is impossible to interpret the result in terms of natural language. The consolidation of neural networks and fuzzy logic in neurofuzzy models provides learning as well as readability. This paper aims at modeling the input-output relationship with fuzzy IF-THEN rules by using fuzzy clustering technique. The main difference between fuzzy cluster...

--Thresholding, the problem of pixel classification is attempted here using fuzzy clustering algorithms. The segmented regions are fuzzy subsets, with soft partitions characterizing the region boundaries. The validity of the assumptions and thresholding schemes are investigated in the presence of distinct region proportions. The hard k means and fuzzy c means algorithms have been found useful w...

A neural network can approximate a function, but it is impossible to interpret the result in terms of natural language. The consolidation of neural networks and fuzzy logic in neurofuzzy models provides learning as well as readability. This paper aims at modeling the input-output relationship with fuzzy IF-THEN rules by using fuzzy clustering technique. The main difference between fuzzy cluster...

A genetic approach is developed, which is suitable for the optimization of fuzzy c-means clustering. The approach is based on real encoding of the prototype variables (cluster centers) and uses appropriate genetic operators and techniques to optimize the clustering criterion. Experimental results concerning diicult clustering problems show that the proposed approach is very successful in genera...