The Image Clustering Technique Used to Find Density of Data Points in an Image using Hill Climbing Algorithm Technique

Images are considered as one of the most important medium of conveying information. Understanding images and extracting the information from them such that the information can be used for other tasks is an important aspect of Machine learning. In this paper various clustering techniques along with some clustering algorithms are described. Further Hill climbing algorithm, its limitations and a new approach of clustering called as M-step clustering that may overcomes these limitations of k-means is included. Image retrieval is the basic requirement task in the present scenario. Content Based Image Retrievalis the popular image retrieval system by which thetarget image to be retrieved based on the useful features of the given image. In other end, image mining is the arising concept which can be used to extract potential information from the general collection of images. Target or close Images can be retrieved in a little fast if it is clustered in a right manner.