In this paper, a possibilistic Hopfield neural network (PHNN) has been proposed for clustering and subsequently applied to brain hemorrhage image segmentation based on a series of CT images. The neural network structure has been implemented by imbedding the weighting possibilistic c-means algorithm into a Hopfield neural network. The network solved the coincidental cluster problem by using a we...

The paper deals in a preliminary way with the problem of the selection of a unique fuzzy preference relation from the set of fuzzy preference relations. A direct algorithm of possibilistic clustering is the basis of the method of fuzzy preference relation discriminating. A method of decision-making based on a fuzzy preference relation is described and basic concepts of the heuristic method of p...

In search of semantic richer and more flexible database modelling and database querying techniques, different approaches based on fuzzy set theory have been developed. Among the most successful approaches are the possibilistic and similarity based models. More recently, extended possibilistic logic and various extensions to fuzzy sets have been applied to further enrich flexible database models...

Propagating possibilistic and probabilistic variables through a mapping yields a fuzzy random variable. We propose a method to attach probability intervals to events pertaining to the output variable. We show that this method is consistent with classical approaches to fuzzy random variables and that the obtained probability interval is the mean value of the fuzzy probability defined by viewing ...

Intention recognition has significant applications in ambient intelligence, assisted living and care of the elderly, games and intrusion and other crime detection. In this chapter we explore an approach to intention recognition based on clustering. To this end we show how to map the intention recognition problem into a clustering problem. We then use three different clustering algorithms, Fuzzy...

Data mining technology has emerged as a means for identifying patterns and trends from large quantities of data. Data mining is a computational intelligence discipline that contributes tools for data analysis, discovery of new knowledge, and autonomous decision making. Clustering is a primary data description method in data mining which group’s most similar data. The data clustering is an impor...

An overview of fuzzy c-means clustering algorithms is given where we focus on different objective functions: they use regularized dissimilarity, entropy-based function, and function for possibilistic clustering. Classification functions for the objective functions and their properties are studied. Fuzzy c-means algorithms using kernel functions is also discussed with kernelized cluster validity...

A generalized hybrid unsupervised learning algorithm, which is termed as rough-fuzzy possibilistic c-means (RFPCM), is proposed in this paper. It comprises a judicious integration of the principles of rough and fuzzy sets. While the concept of lower and upper approximations of rough sets deals with uncertainty, vagueness, and incompleteness in class definition, the membership function of fuzzy ...

image segmentation is an essential issue in image description and classification. currently, in many real applications, segmentation is still mainly manual or strongly supervised by a human expert, which makes it irreproducible and deteriorating. moreover, there are many uncertainties and vagueness in images, which crisp clustering and even type-1 fuzzy clustering could not handle. hence, type-...