Timeline for Low silhouette coefficient
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Aug 4, 2020 at 14:21 | comment | added | Dimitrios Panagopoulos | I am a bit confused. What does each row in your dataset represents? The selling values of all articles for a specific date/week? If that is the case, then what you are trying to do is to cluster the various dates/weeks? | |
| Aug 8, 2018 at 7:01 | comment | added | ItFreak | well thats bad:/ | |
| Aug 8, 2018 at 6:59 | comment | added | Has QUIT--Anony-Mousse | If every article is its own cluster, the Silhouette by definition is 0. | |
| Aug 8, 2018 at 6:56 | comment | added | ItFreak | I visualized some of the article selling values and there were different shapes, so there must be clusters. my suggestion is that the silhouette coefficient converges towards the number of article numbers and gets maximum if every article number is in its own cluster. i normalized the data because giving the data_percentage_change to the kmeans.fit() gave me a weird result with worng scales on x and y axis. how can i pass this dataframe to kmeans so he treats each columns as 'data' from top to down? | |
| Aug 7, 2018 at 19:22 | comment | added | Has QUIT--Anony-Mousse | Bad silhouette can also be cause by data that just does not have clusters. Are you sure there are clusters? Have you tried visualizing? | |
| Aug 7, 2018 at 19:22 | comment | added | Has QUIT--Anony-Mousse | Blind "normalizing" barely does any good. You'd better do this in an informed way. Here, theoretical understanding may tell you to not normalize this at all. | |
| Aug 6, 2018 at 19:32 | history | answered | Has QUIT--Anony-Mousse | CC BY-SA 4.0 |