Timeline for Euclidean space vs metric space in density clustering algorithms
Current License: CC BY-SA 4.0
4 events
| when toggle format | what | by | license | comment | |
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| Jun 16, 2021 at 18:53 | comment | added | D.W.♦ | This is a nice exercise. If you understand each of those algorithms, it should be a mechanical process to work through each line of pseudocode to verify whether it can be done in an arbitrary metric space. That seems like something you should be able to do yourself. What progress have you made and is there any specific uncertainty you've run into as you do that analysis? | |
| Jun 16, 2021 at 16:37 | comment | added | Inuyasha Yagami | However, you can use $k$-means++ algorithm in general metric spaces. It is as good as the $k$-means algorithm. | |
| Jun 16, 2021 at 16:36 | comment | added | Inuyasha Yagami | $k$-means algorithm will not work directly in the metric spaces because you need to find the centroid of a set of points. In general metric spaces, there is no concept of the centroid. See here for more discussion. | |
| Jun 16, 2021 at 10:57 | history | asked | FlubberBeer | CC BY-SA 4.0 |