Timeline for Finding cluster number based on distance & max element count
Current License: CC BY-SA 3.0
5 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jun 26, 2015 at 20:15 | comment | added | sophistry | Ended up using a heuristic algorithm that checks distance and max cluster elements after each k-means cycle. It's computationally expensive, but gets the job done for now. | |
| Jun 26, 2015 at 20:13 | vote | accept | sophistry | ||
| Jun 18, 2015 at 18:49 | comment | added | Has QUIT--Anony-Mousse | I'm not suggesting to do that. This is just the outline of a proof that your problem is np-hard, so you may want to relax the constraints and search for an approximation. Just like k-means does: finding the true minimum is too expensive. | |
| Jun 18, 2015 at 18:41 | comment | added | sophistry | In this case, I want to use these two initial conditions to approximate a minimum value for 'k' in a k-means or k-medoids algorithm. You suggest first finding all possible clusters, then computing the minimum number a la set cover problem. For your approach, what would be the best clustering method to do so? | |
| Jun 17, 2015 at 22:01 | history | answered | Has QUIT--Anony-Mousse | CC BY-SA 3.0 |