… includes two steps: a) Segmentation step b) Classification … image segmentation and
classification done using K-Means and Laplacian of Gaussian filters or Prewitt Filter. Our algorithm …

… images has become important. This paper proposes an automatic medical image classification
method to classify two … We have used k-means clustering algorithm to segment the lesion. …

… In this paper, we present an approach for HSI classification using PCA, k-means clustering
and M-SVM. The approach is compared with an existing technique that uses PCA and MSVM. …

… of standard kmeans and fuzzy k-means clustering algorithms in the area of image … or
data grouping describes important technique of unsupervised classification that arranges …

… The goal of proposed work is to diagnose the disease of brinjal leaf using image processing
… includes Kmeans clustering algorithm for segmentation and Neural-network for classification…

… scheme for the classification of MRI brain tumor images using K-means clustering, NSCTand
… filtering is used for removing noise and enhancing the resolution of MRI brain images. Then …

… , Classification analysis, Artificial intelligent, image processing and machine vision. K-Means
makes hard assignments of points to clusters … of K-Means clustering method for color image …

… —Clustering is a major technique used for grouping of numerical and image data in data
mining and image processing … Clustering is the unsupervised classification of patterns such as …

… RBF/K-means algorithm The RBF/K-means algorithm considers that we have K classes to
recognize the N samples exj . Sj is the expected output from neuron j (0 or 1) and fj (x) the e …

… is k-means clustering algorithm. K-means clustering algorithm is an unsupervised algorithm
… , image processing, traffic image, pattern recognition etc. There are different techniques for …