Customer Segmentation for Telecom with the k-means Clustering Method

Image Segmentation With Modified K-Means Clustering Method

Image segmentation is used to recognizing some objects or something that is more meaningful and easier to analyze In this paper we are focus on the the K means clustering for segmentation of the image. K-means clustering is the most widely used clustering algorithm to position the radial basis function (RBF) centres. Its simplicity and ability to perform on-line clustering may inspire this choi...

K - Means Clustering for Automatic Image Segmentation

Application of K-Means Algorithm for Efficient Customer Segmentation: A Strategy for Targeted Customer Services

The emergence of many business competitors has engendered severe rivalries among competing businesses in gaining new customers and retaining old ones. Due to the preceding, the need for exceptional customer services becomes pertinent, notwithstanding the size of the business. Furthermore, the ability of any business to understand each of its customers’ needs will earn it greater leverage in pro...

An Adaptive K-means Clustering Algorithm for Breast Image Segmentation

Breast cancer is one of the major causes of death among women. Small clusters of micro calcifications appearing as collection of white spots on mammograms show an early warning of breast cancer. Early detection performed on X-ray mammography is the key to improve breast cancer diagnosis. In order to increase radiologist’s diagnostic performance, several computer-aided diagnosis (CAD) schemes ha...

An Improved K-means Clustering Algorithm for Image Segmentation

Image segmentation is a primary step in many computer vision applications, whose purpose is to extract information from the images to allow the discrimination among different objects of interest. This task usually involves the partitioning of the image into a number of clusters, such that the data in each cluster share similar features. This work describes a new clustering algorithm for providi...