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

The k-means algorithm is known to have a quadratic time complexity in terms of n, the input datasize. 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 andexecution time) of the classical k-means algorithm which requires comparatively lesser number ofiterations. The underlying modification includes a gradual directional movement of intermediateclusters and thereby improving compactness and separability properties of cluster structuressimultaneously. This process also results in an improved visualization of clustered data. Experimentalresults using various types of data sets prove our claim. Comparison of results obtained with theclassical k-means and the present algorithm indicates usefulness of the new approach.

کلیدواژه ها:

ClusteringIteration Efficiencyk-meansLinear-time-complexityVisualization