I have spatial data(2D) with some quantity associated with each point - basically 3D data. I want to model the quantity distribution in the space and then use N clusters as a compact representation. My idea is to interpolate with a spline surface over the points, then scale the quantity at each point so that the integral of the spline be equal to 1. How can I find the best approximation of the spline using a weighted sum of N(fixed number) gaussians? I want to use then the means of the gaussians as cluster centers.

PS: Do you think that there is a better approach to the problem?

Regards, T

  • $\begingroup$ My first thought would have been to do a (Gaussian) kernel density estimate directly on the data rather than try to approximate the approximation to the data. $\endgroup$ – Glen_b Jun 30 '17 at 1:35
  • $\begingroup$ I had the same idea, but it is not the think I want. The problem is I need to do a spatial(in the plane) clustering of the quantity data in a such manner that when done I get roughly simmilar clusters of the quantity originating in compact areas.... $\endgroup$ – Todor Kostov Jul 1 '17 at 13:56

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