Learning the k in k-means

Greg Hamerly and Charles Elkan
CS2002-0716
July 30, 2002

When clustering a dataset, the right number $k$ of clusters to use is often not obvious, and choosing k automatically is a hard algorithmic problem. In this paper we present a new algorithm for choosing k that is based on a new statistical test for the hypothesis that a subset of data follows a Gaussian distribution. The algorithm runs k-means with increasing k until the test fails to reject the hypothesis that the data assigned to each k-means center are Gaussian. We present results from experiments on synthetic and real-world data showing that the algorithm works well, and better than a recent method based on the BIC penalty for model complexity.


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