Data mining is the process of extracting hidden patterns from huge data. Among the various clustering algorithms, k-means is the one of most widely used clustering technique in data mining. The performance of k-means clustering depends on the initial clusters and might converge to local optimum. K-means does not guarantee the unique clustering because it generates different results with randoml...

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...

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...

This Brain tumors are the mechanisms to control normal cells randomly and uncontrolled multiplication of cells in which growth is an abnormal mass of tissue. A tumor growth takes place within the skull and interferes with normal brain activity. Therefore, the first step is very important in tumor detection. Various techniques have been developed to detect tumors in the brain. Most crucial task ...

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 ...