In this paper , The intelligent hybrid methods are used for improving the performance of K-means and Cmeans algorithms. . To achieve this, these methods are explained in order to improve the performance of these two data mining algorithms. Some suggestions are provided for this aim. The methods used for explaining in relation to C-means algorithms are fuzzy C-means algorithm, combination of fuz...

This paper details the implementation of three traditional clustering techniques (K-Means clustering, Fuzzy CMeans clustering and Adaptive K-Means clustering) that are applied to extract the colour information that is used in the image segmentation process. The aim of this paper is to evaluate the performance of the analysed colour clustering techniques for the extraction of optimal features fr...

A new online clustering method, called E2GK (Evidential Evolving Gustafson-Kessel) is introduced in the theoretical framework of belief functions. The algorithm enables an online partitioning of data streams based on two existing and efficient algorithms: Evidantial cMeans (ECM) and Evolving Gustafson-Kessel (EGK). E2GK uses the concept of credal partition of ECM and adapts EGK, offering a bett...

Clustering is an important technique for data mining which allows us to discover unknown relationships in our data sets. Clustering algorithms that use metrics based on the natural ordering of numbers cannot be applied to categorical (non-numerical) data. In this tutorial we will review the main methods for numerical data clustering (K-Means, Hierarchical Clustering and Fuzzy CMeans) and then s...

Data clustering is a process of putting similar data into groups. A clustering algorithm partitions a data set into several groups such that the similarity within a group is larger than among groups. This paper reviews four of the most representative off-line clustering techniques: K-means clustering, Fuzzy Cmeans clustering, Mountain clustering, and Subtractive clustering. The techniques are i...

In this paper we present an automatic algorithm for segmenting the putamen from brain MRI based on wavelets and neural network. We first locate the position of putamen using wavelet features. The fuzzy cmeans algorithm is then combined with edge detection to segment the grey matter pixels belonging to the putamen in the located region. Moment features are extracted from the segmented objects fo...

Unsupervised change detection using fuzzy cmeans and MRF from remotely sensed images Ming Hao, Hua Zhang, Wenzhong Shi & Kazhong Deng To cite this article: Ming Hao, Hua Zhang, Wenzhong Shi & Kazhong Deng (2013) Unsupervised change detection using fuzzy c-means and MRF from remotely sensed images, Remote Sensing Letters, 4:12, 1185-1194, DOI: 10.1080/2150704X.2013.858841 To link to this article...

In this paper, we study a new type of clustering problem, called Chromatic Clustering, in high dimensional space. Chromatic clustering seeks to partition a set of colored points into groups (or clusters) so that no group contains points with the same color and a certain objective function is optimized. In this paper, we consider two variants of the problem, chromatic k-means clustering (denoted...

The selection of centers and widths has a strong influence on the performance of radial basis function neural network classifier. In this paper, a novel approach of clustering based on Fuzzy Cmeans clustering is proposed, which is called cooperative clustering, and use it for selection of centers of radial basis function neural network. Experimental results show that the performance of classifi...