Intuitionistic fuzzy sets are generalized fuzzy sets whose elements are characterized by a membership, as well as a non-membership value. The membership value indicates the degree of belongingness, whereas the nonmembership value indicates the degree of non-belongingness of an element to that set. The utility of intuitionistic fuzzy sets theory in computer vision is increasingly becoming appare...

This paper presents a detailed study and comparison of some Kernelized Fuzzy C-means Clustering based image segmentation algorithms Four algorithms have been used Fuzzy Clustering, Fuzzy CMeans(FCM) algorithm, Kernel Fuzzy CMeans(KFCM), Intuitionistic Kernelized Fuzzy CMeans(KIFCM), Kernelized Type-II Fuzzy CMeans(KT2FCM).The four algorithms are studied and analyzed both quantitatively and qual...

Image segmentation is one of the most common steps in digital image processing. The area many image segmentation algorithms (e.g., thresholding, edge detection, and region growing) employed for classifying a digital image into different segments. In this connection, finding a suitable algorithm for medical image segmentation is a challenging task due to mainly the noise, low contrast, and steep...

Medoids-based fuzzy relational clustering generates clusters of objects based on relational data, which records pairwise similarity or dissimilarities among objects. Compared with single-medoid based approaches, multiple-weighted medoids has shown superior performance in clustering. In this paper, we present a new version of fuzzy relational clustering in this family called fuzzy clustering wit...

nowadays, wireless sensor network has been of interest to investigators and the greatest challenge in this part is the limited energy of sensors. sensors usually are in the harsh environments and transit in these environments is hard and impossible and moreover the nodes use non- replaceable batteries. because of this, saving energy is very important. in this paper we tried to decrease hard and...

The objective of the present paper is to describe a pattern recognition approach for image segmentation using fuzzy clustering. Soft computing techniques have found wide applications. One of the most important applications is edge detection for image segmentation. Clustering analysis is one of the major techniques in pattern recognition. These fuzzy clustering algorithms have been widely studie...

Combined with weight of samples and kernel function, fuzzy clustering method with generalized entropy is studied. Objective function for fuzzy clustering with generalized entropy based on sample weighting is obtained. Following that, fuzzy clustering algorithm with generalized entropy based on sample weighting is presented. In addition, by introducing kernel into the presented objective functio...

Traditional spatial data are generally high dimensional features, and in the clustering of high dimensional data can be directly applied to data processing because of Dimension effect and the data sparseness problem. For CLIQUE algorithm, which usually have the problem such as prone to non-axis direction of overclustering, boundary judgment of fuzzy clustering and smoothing clustering. In this ...

This paper introduces an additive fuzzy clustering model for similarity data as oriented towards representation and visualization of activities of research organizations in a hierarchical taxonomy of the field. We propose a one-by-one cluster extracting strategy which leads to a version of spectral clustering approach for similarity data. The derived fuzzy clustering method, FADDIS, is experime...

This paper presents a new hierarchical tree approach to clustering fuzzy data, namely extensional tree (ET) clustering algorithm. It defines a dendrogram over fuzzy data and using a new distance between fuzzy numbers based on -cuts. The present work is based on hierarchical clustering algorithm unlike existing methods which improve FCM to support fuzzy data. The Proposed ET clustering algorithm...