Clustering in data mining is very important to discover distribution patterns and this importance tends to increase as the amount of data grows. It is one of the main analytical methods in data mining and its method influences its results directly. K-means is a typical clustering algorithm[3]. It mainly consists of two phases i.e. initializing random clusters and to find the nearest neighbour. ...

This study has applied digital image processing on three-dimensional C.T. images to detect and diagnose kidney diseases. Medical of different cases diseases were compared with those healthy cases. Four kidneys disorders, such as stones, tumors (cancer), cysts, renal fibrosis considered in additional tissues. method helps differentiating between the diseased It can its very early stages, before ...

An abnormal rise in glucose levels may lead to diabetes. Around 30 million people are diagnosed with this disease our country. In perspective Indian Council of Medical Research funded by Registry People diabetes India have taken an initiative and come up numerous solutions but unfortunately neither them has shape. Initially, the behavior chemical reaction between agent is estimated tracked regi...

In addition to finding meaningful clusters, centroid-based clustering algorithms such as K-means or mean-shift should ideally find centroids that are valid patterns in the input space, representative of data in their cluster. This is challenging with data having a nonconvex or manifold structure, as with images or text. We introduce a new algorithm, Laplacian K-modes, which naturally combines t...