A Fast k-means Algorithm using Cluster Shifting to Produce Compact and Separate Clusters

The k-means algorithm is known to have a quadratic time complexity in terms of n, the input data size. this quadratic complexity debars the algorithm from being effectively used in large applications. in this article, an attempt is made to develop a faster version (in terms of both number of iterations and execution time) of the classical k-means algorithm which requires comparatively lesser number of iterations. the underlying modification includes a gradual directional movement of intermediate clusters and thereby improving compactness and separability properties of cluster structures simultaneously. this process also results in an improved visualization of clustered data. experimental results using various types of data sets prove our claim. comparison of results obtained with the classical k-means and the present algorithm indicates usefulness of the new approach