Approaching optimal solution by combining K-means clusterings

School of Computing, University of Eastern Finland

30.8.2012, Conference room TB330

10.00-10:45
Mohammad Rezaei
Univ. Eastern Finland

Applying K-means to a dataset with different initial centroids, usually produce different clusterings. Each clustering can have some benefits regarding to finding some of the correct centroids. In this paper we propose a method for constructing a new clustering from two clusterings which includes the benefits of them. According to merge and split costs (computed for each cluster in one clustering according to the other one), some centroids are replaced with centroids from the other clustering. In an iterative algorithm, the resulting clustering is considered with a new clustering produced by K-means. If there would be naturally separated clusters in a dataset, the proposed method can find optimal clustering in a few iterations.