Timeline for Difference between Hartigan & Wong Algo to Lloyd's algorithm in K-means clustering
Current License: CC BY-SA 4.0
5 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jul 27, 2018 at 14:31 | comment | added | Arpit Sisodia | seems like some more research is required before moving forward. :( | |
| Jul 26, 2018 at 19:20 | comment | added | Has QUIT--Anony-Mousse | And the paper you linked above is wrong to suggest using all these metrics with k-means. It's easy to find counterexamples that k-means will not find a (local) optimal solution with Euclidean or Manhattan distance. It only finds local optima with squared Euclidean and other Bergman divergences (a fairly narrow class). See the many other questions on k-means and other metrics. | |
| Jul 26, 2018 at 19:13 | comment | added | Has QUIT--Anony-Mousse | No, MacQueen does not take the negative effects into account. He does an incremental Single-Pass that will often yield fairly bad results. | |
| Jul 26, 2018 at 12:50 | comment | added | Arpit Sisodia | As I have read on paper mentioned, it clearly says Lloyd's take one of the distance metrics. Your explanation of Hartigan seems like Macqueen algo. :) | |
| Jul 26, 2018 at 5:59 | history | answered | Has QUIT--Anony-Mousse | CC BY-SA 4.0 |