Timeline for How to tell quantitatively whether 1D data is clustered around 1 or 3 values?
Current License: CC BY-SA 3.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jan 2, 2012 at 18:09 | comment | added | Has QUIT--Anony-Mousse | They clearly look Gaussian enough to me. K-means models data with Voronoi cells. It does not seem sensible to me to assume that the best split point is exactly in the middle of the two neighboring means. | |
| Jan 2, 2012 at 13:53 | vote | accept | Nikolaus | ||
| Jan 2, 2012 at 12:37 | comment | added | Elvis | +1 to this and to Greg Snow. I totally agree with this advice. @Nikolaus I think this looks "gaussian enough" to fit a mixture of gaussians distributions. You don’t want a perfect fit, just a way to check how many clusters there are. In this optic, constraining all components to share the same standard deviation can be a good idea (for the reasons explained by Anony-Mousse). | |
| Jan 2, 2012 at 11:57 | comment | added | Nikolaus | Thanks, I didn't know of Levenberg-Marquardt! These clusters are not Gaussian; do you still think Gaussian functions would be the best PDF to fit them to? | |
| Jan 2, 2012 at 10:54 | history | edited | Has QUIT--Anony-Mousse | CC BY-SA 3.0 |
added 139 characters in body
|
| Jan 2, 2012 at 10:01 | history | answered | Has QUIT--Anony-Mousse | CC BY-SA 3.0 |