Seed selection strategies for K-means

I wonder what kind of seed selection methods I can apply to K-means 开发者_如何学编程algorithm. Google search wasn't that helpful. Any suggestions?

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The seeds depend on the domain. For example, if your data items are words, your seeds should be the most frequent words. Otherwise, you could cluster a small sample and use that as a seed.

Here is an example of a more sophisticated algorithm:

Single Pass Seed Selection Algorithm for k-Means. K. Karteeka Pavan, Allam Appa Rao, A.V. Dattatreya Rao and G.R. Sridhar. Journal of Computer Science 6 (1): 60-66, 2010. pdf

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Google for "supervised" k means clustering & k++ means.... also specify your performance needs ( whats your k? how many input points?)

In general, a few thousand points can easily be clustered w a naive k means algorithm implementation... So I would try that first.

Also, if your not sure what K should be, try MCL clustering first to get a good estimate.