2014-04-11 10:55 GMT+02:00 Daniel Vainsencher <[email protected]>: > In any case, the approximate nature of the search raises the possibility > of going a step further: index the data points, and adjust each cluster > to its ANNs (in this case, for a very long list of candidates). This is > no longer k-means (closer to a mean-shift algorithm) and may or may not > work, but could be very fast.
Speaking of, mean-shift is already implemented using NN. Judging from GitHub issues, ML questions and the complexity notes in the mean-shift docstrings, I also believe that optimizing it would be more valuable than optimizing k-means, since we already have minibatch k-means. (Also k-means can still benefit from the Elkan optimization, which doesn't change its semantics.) ------------------------------------------------------------------------------ Put Bad Developers to Shame Dominate Development with Jenkins Continuous Integration Continuously Automate Build, Test & Deployment Start a new project now. Try Jenkins in the cloud. http://p.sf.net/sfu/13600_Cloudbees _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
