A Linear Time-Complexity k-Means Algorithm Using Cluster Shifting

The k-means algorithm is known to have a time complexity of O(n2), where n is 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 an O(n) complexity (linear order) counterpart of the k-means. The underlying modification includes a directional movement of intermediate clusters and thereby improves compactness and separability properties of cluster structures simultaneously. This process also results in an improved visualization of clustered data. Comparison of results obtained with the classical k-means and the present algorithm indicates usefulness of the new approach.