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An implementation of the x-means algorithm in Go.

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Here you use per-cluster variances: https://github.com/bobhancock/goxmeans/blob/a78e909e374c6f97ddd04a239658c7c5b7365e5c/km.go#L788-L791 However both the x-means paper, as well as your BIC_notes.pdf use the assumption that all clusters should have the same variance. That should resolve...

As discussed here: https://stats.stackexchange.com/a/146539/18215 (first comment by user Mikoyan)

Increment K in leaps, as mentioned in person

‘gaussrandom’: generate k centroids from a Gaussian with mean and variance estimated from the data. ‘uniform’: generate k observations from the data from a uniform distribution defined by the data...