Data analysis and classification play a big role in understanding various real life phenomena. Clustering helps analyze data with little or no prior knowledge about it. K-means clustering is a popular clustering algorithm with applications to computer vision, data mining, data visualization, etc.. Due to continuously increasing data volume, parallel computing is necessary to overcome the computational challenges involved in K-means clustering. We present the design and implementation of Kmeans clustering algorithm on widely available graphics processing units (GPUs), which have the required hardware architecture to meet these parallelism needs. We analyze the scalability of our proposed methods with increase in number and dimensionality of data points as well as the number of clusters. We also compare our results with current best available implementations on GPUs and a 24-way threaded parallel CPU implementation. We achieved a consistent speedup of 6.5x over the parallel CPU implementation.