###In k-means, a data point is comprised of several values, called features. By dividing a cluster of data objects into k sub-clusters, k -means represents all the data objects by the mean values or centroids of their respective sub-clusters.Since the complexity and time taken by the sequential compiler is high it is proposed to use the GPU for parallelization to the maximum extent possible.
The traditional sequential KMeans Algorithm is of com- plexity O(nk) where n is the number of points and k is the number of clusters. In the new algorithm prroposed the complexity will be O(nk/p) where p is the degree of paral- lelism. The bjective of the project is to implement parallel version of k means image segmentation algo- rithm using CUDA parallel programming language.