Timeline for Choosing the right linkage method for hierarchical clustering
Current License: CC BY-SA 3.0
10 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| May 17, 2019 at 17:40 | answer | added | kakarot | timeline score: 6 | |
| Jun 7, 2016 at 12:04 | history | edited | ttnphns | CC BY-SA 3.0 |
added 116 characters in body; edited tags
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| Jun 7, 2016 at 11:55 | answer | added | ttnphns | timeline score: 97 | |
| Feb 16, 2016 at 17:42 | comment | added | Kevbot | @ttnphns, thanks for the link - was a good read and I'll take those points in to consideration. | |
| Feb 14, 2016 at 10:35 | review | Close votes | |||
| Feb 14, 2016 at 12:03 | |||||
| Feb 14, 2016 at 10:18 | comment | added | ttnphns | Kevin, Please have a look on this answer and this very recent question. You will learn that the question ("what method to use") you are rising is not easy one. You should definitely read literature about clustering (at least hierarchical) before you can see the difference between methods and be able to choose. Data analysis is not to be treated off-handly. | |
| Feb 14, 2016 at 6:16 | comment | added | Kevbot | Best for me is finding the most logical way to link my kind of data. ie: what approach accurately defines what is meant by "distance" within my features. | |
| Feb 13, 2016 at 22:24 | comment | added | gung - Reinstate Monica | Setting aside the specific linkage issue, what would "best" mean in your context? | |
| Feb 13, 2016 at 22:09 | review | First posts | |||
| Feb 13, 2016 at 22:24 | |||||
| Feb 13, 2016 at 22:09 | history | asked | Kevbot | CC BY-SA 3.0 |