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.