In this paper, we introduce the evolutionary design methodology of fuzzy inference systems by means of fuzzy partition of input space. The rules of the proposed fuzzy model are realized with the aid of the fuzzy partition of input space generated by fuzzy c-means clustering algorithm. The number of the partition of input space is equal to the number of clusters. And the individual partitioned s...

In this work we describe a microdata protection method based on the use of fuzzy clustering and, more specifically, using fuzzy c-means. Microaggregation is a well-known masking method for microdata protection used by National Statistical Offices. Given a set of objects described in terms of a set of variables, this method consists on building a partition of the objects and then replace the ori...

This paper presents a comparison of the three fuzzy based image segmentation methods namely Fuzzy C-Means (FCM), TYPE-II Fuzzy C-Means (T2FCM), and Intuitionistic Fuzzy C-Means (IFCM) for digital images with varied levels of noise. Apart from qualitative performance, the paper also presents quantitative analysis of these three algorithms using four validity functions-Partition coefficient (Vpc)...

The present work describes a new approach to integrating structural knowledge into the image classification process. First, a fuzzy classifier produces a fuzzy partition of the image. Then, the defuzzified (crisp) partition is tried to be improved. According to the membership degrees in the fuzzy partition, the system selects a set of pixels and associates a set of candidate classes with each o...

In this paper, the texture feature ”coarseness” is modelled by means of a fuzzy partition on the domain of coarseness measures. The number of linguistic labels to be used, and the parameters of the membership functions associated to each fuzzy set are calculated relating representative coarseness measures (our reference set) with the human perception of this texture property. A wide variety of ...

In this paper, the utility of credibilistic critical values in crisp conversion of fuzzy data sets is considered. Conversion of this type becomes essential mainly when clustering of fuzzy data sets is carried out. In this paper performance of two popular clustering algorithms namely Fuzzy c–means and Fuzzy c–medoids algorithms are evaluated under credibilistic critical value crisp conversion is...

In this paper, we present an algorithm to infer a fuzzy partition over a set of numerical values. This algorithm is based on mathematical morphology and is expressed in theory of formal language. It is usable when the numerical values are associated with a class. We use this algorithm during the construction of a fuzzy decision tree, when no fuzzy partition is available for a numerical attribute.

—In this paper we propose a new method to represent information granules by Gaussian functional forms. First, the fuzzy granules are extracted from data by a fuzzy clustering algorithm. Then, they are properly represented by Gaussian functions determined by solving a constrained quadratic programming problem on membership values returned by the clustering algorithm. Simulation results show that...

The aim of this study is the development of tools dedistage, although commonly overlooked (Hammond and Verbyla, 1996). Evaluating a partition generally consists cated to fuzzy partition evaluation in the field of satellite image classification. While a traditional crisp partition of comparing it with a control partition and measuring only provides qualitative information, a fuzzy partition rept...