2011/12/2 María Helena Mejía Salazar <[email protected]>: > Hi, > > I modified a little bit the program of demo dbscan (plot_dbscan.py). I am > using just distance (no similarities) and I am having bad results. There are > just 5 points, I changed the eps as the minimum distance between the > points and the number of minimun points are 2 since this is what I requiered > for doing the cluster. I am getting that all the points are noise. > I used WEKA (java) too and it produced the desired results.
I have little understanding of the DBSCAN algorithm itself but clustering 5 datapoints sounds like an hill-defined task to me. Are you sure this is what you are looking for? Maybe a full hierarchical clustering tree (the dendrogram) would be more interesting in this case. -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ All the data continuously generated in your IT infrastructure contains a definitive record of customers, application performance, security threats, fraudulent activity, and more. Splunk takes this data and makes sense of it. IT sense. And common sense. http://p.sf.net/sfu/splunk-novd2d _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
