Timeline for Estimating the number of clusters
Current License: CC BY-SA 2.5
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Aug 28, 2010 at 21:43 | comment | added | ABh | Actually, this "Barcodes" paper points to projection of high-dimensional datasets into lower dimensions and then recognizing shapes. The projection vector used to decrease the dimensionality of the data points has a strong effect on the possible separability of the clusters. I actually don't know if there's a good way to pre-estimate the number of expected clusters without considering a priori factors about what the data set it. But the paper does not necessarily address what the best projective vector might be. | |
| Oct 22, 2009 at 2:00 | vote | accept | Kim Greene | ||
| Oct 22, 2009 at 2:00 | |||||
| Oct 21, 2009 at 17:48 | comment | added | Josh | Yes, that's true. | |
| Oct 21, 2009 at 16:06 | comment | added | Darsh Ranjan | Isn't persistent 0th homology pretty relevant to the OP's problem? | |
| Oct 21, 2009 at 11:37 | comment | added | Josh | I don't think persistent homology actually would help with this. The question doesn't seem to be about data analysis, but clustering of data. | |
| Oct 21, 2009 at 3:33 | history | answered | Reid Barton | CC BY-SA 2.5 |