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.
The authors of these documents have submitted their reports to this technical report series for the purpose of non-commercial dissemination of scientific work. The reports are copyrighted by the authors, and their existence in electronic format does not imply that the authors have relinquished any rights. You may copy a report for scholarly, non-commercial purposes, such as research or instruction, provided that you agree to respect the author's copyright. For information concerning the use of this document for other than research or instructional purposes, contact the authors. Other information concerning this technical report series can be obtained from the Computer Science and Engineering Department at the University of California at San Diego, email@example.com.
[ Search ]