Variance-based clustering for balanced clusters in growing datasets

Abstract

k-Means is a very popular clustering algorithm. We modify its objective to achieve a clustering method which produces more balanced clusters. The proposal can be adapted in a framework where dataset keeps growing and number of clusters is decided within the algorithm to achieve balanced clustering. This is done without affecting the time complexity. Experimental results are in favor of the proposal.