On Tue, Jul 17, 2018 at 04:07:15AM +0000, Yew Khong See wrote: > Hi all, > I am using DBSCAN to cluster a dataset consisting of an individual's > weight (in kg) and height (in cm). > What I am doing now is to cluster the weights first and then do > another clustering on the heights from each weight cluster. > This method is not efficient and will not scale with larger datasets. > > Is there a better way to perform clustering one time on both the > weights and heights, but with different epsilon and minpoints?
Hi there, Can you clarify what you mean by 'different minpoints'? I can picture what you mean when you say 'different epsilon'---I think that you mean that you want a different epsilon value for weight and height, and that you want to cluster simultaneously using both weight and height values. In this case you could just normalize your data accordingly: if, e.g., you want epsilon 1 for weight and 2 for height, simply divide all the height values by 2, and then use epsilon = 1. Hope this helps; let me know if I can clarify further. Thanks, Ryan -- Ryan Curtin | "Indeed!" [email protected] | - David Lo Pan _______________________________________________ mlpack mailing list [email protected] http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack
