We give a unifying description of several least squares clustering methods. We consider the K-means/Lloyd’s algorithm, various local search based methods and clustering techniques based on neural networks. The relations between the various algorithms are explained. Also some computational results of these algorithms are given. Mathematics Subject Classifications (2000): 62H30, 68T05-20, 90C59, ...

Cluster analysis has received attention in many scientific fields. The purpose of clustering analysis is to detect group data points, which are close to one another. One of the most widely used techniques for clustering is the K-means algorithm. The performance of K-means algorithm which converges to numerous local minima depends highly on initial cluster centers. In order to overcome local opt...

We present a stereo image denoising algorithm. Our algorithm takes as an input a pair of noisy images of an object captured from two different directions (stereo images). We use either Maximum Difference or Singular Value Decomposition similarity metrics for identifying locations of similar searching windows in the input images. We adapt the Non-local Means algorithm for denoising collected pat...

Clustering technology has received a lot of concern in many areas such as engineering, medicine, biology and data mining. Collecting data points is the purpose of clustering and the most common clustering technology is K-means algorithm. However, results of kmeans depend on the initial state and convergence to a local optimum is also its drawback. To overcome these drawbacks, many studies have ...

The recently introduced non-local means (NLM) image denoising technique broke the traditional paradigm according to which image pixels are processed by their surroundings. Non-local means technique was demonstrated to outperform state-of-the art denoising techniques when applied to images in the visible. This technique is even more powerful when applied to low contrast images, which makes it tr...