Here’s a small kmeans that uses any of the 20-odd distances in scipy.spatial.distance, or a user function. Comments would be welcome (this has had only one user so far, not enough); in particular, what are your N, dim, k, metric ? #!/usr/bin/env python # kmeans.py using any of the 20-odd metrics in scipy.spatial.distance # kmeanssample … Read more
You can maximize the Bayesian Information Criterion (BIC): BIC(C | X) = L(X | C) – (p / 2) * log n where L(X | C) is the log-likelihood of the dataset X according to model C, p is the number of parameters in the model C, and n is the number of points in … Read more
If your question is “how can I determine how many clusters are appropriate for a kmeans analysis of my data?“, then here are some options. The wikipedia article on determining numbers of clusters has a good review of some of these methods. First, some reproducible data (the data in the Q are… unclear to me): … Read more