![](/profiles/wavemetrics/themes/wavemetrics/logo.png)
What parameters best characterize a cluster in a 2-D data set?
![](/sites/default/files/styles/thumbnail/public/jjweimer/profile-images/2018-12/meimage.jpg?itok=gNUlNM01)
I am comparing output performance on test cases. The two parameters that I track as output performance are relative accuracy RA and relative uncertainty RU. I show an example plot for distributions obtained plotting RA versus RU from 900 samples in each of five different test cases I would like to obtain parametric measures on the distributions to chart the five cases.
The samples are brute force curve fitting to five different equations that should fit a data set. The T form is the non-linear regression fitting. The A - D forms are linearized fitting models. I generate raw theory, add Gaussian noise to the raw data, and perhaps chop out a front end portion of the data as computational ways to mimic experimental approaches to obtain the data. I envision mapping perhaps three parameters for each of the five clusters. I think about this as plotting three characters of a "best fit" ellipse ... centroid (center of mass), aspect, and rotation.
I am hitting the wall of my complete ignorance on the best approach here. Recommendations would be greatly appreciated.
![cluster sets](/sites/default/files/styles/content_body/public/2024-04/StatComparisons.png?itok=G-ggezz3)
If you use the FPClustering operation you may be able to characterize your clusters using the inter-cluster distances in the wave M_InterClusterDistance.
AG
April 17, 2024 at 03:33 pm - Permalink
Thanks AG. I found in the meantime that my data was incorrect. I was using the wrong index from a multi-dimensional matrix. A correct representation is below. I have decided simply to extract averages and standard uncertainties in the two coordinate frames (relative accuracy and relative uncertainty) as metrics from each distribution. I can plot those as a function of my input control parameters.
May 3, 2024 at 06:20 pm - Permalink