Timeline for k-mean without label [closed]
Current License: CC BY-SA 4.0
17 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jan 1, 2019 at 14:25 | comment | added | Has QUIT--Anony-Mousse | If you don't have the vocabulary, how do you expect an algorithm to come up with words?!? | |
| Dec 31, 2018 at 15:06 | comment | added | theantomc | @Wacax unfortunatly I need use k-means for requirements. I see many example like my case, but have different Tf-idf, because starting from dataset. I m starting from tf-idf matrix, so missing vocabulary | |
| Dec 31, 2018 at 14:59 | comment | added | wacax | @theantomc then the approach is erroneous and for that reason I'm going to vote to keep the question closed. If your intention is to cluster word meanings then you should look for approaches like word embeddings. If you want to cluster documents or users in your case, then k-means becomes a good option as each word becomes a feature to be measured for a distance. Check out this article about word embeddings multithreaded.stitchfix.com/blog/2015/03/11/… it might help with what you are trying to do. | |
| Dec 31, 2018 at 14:25 | comment | added | theantomc | i m try to cluster word, and the output i think will be a list of similar word for similar topic | |
| Dec 31, 2018 at 13:54 | comment | added | wacax | @theantomc are you trying to cluster words or users. In the edit you proposed it says you are trying to cluster words, for which k-means would be the wrong method but if it's user what you are trying to cluster then this question could be edited to reflect that intention. | |
| Dec 31, 2018 at 0:15 | review | Reopen votes | |||
| Jan 1, 2019 at 16:02 | |||||
| Dec 30, 2018 at 23:57 | history | edited | theantomc | CC BY-SA 4.0 |
added 470 characters in body
|
| Dec 30, 2018 at 23:43 | comment | added | theantomc | maybe I have to reformulate the question. Unfortunately I saw only this example and I saw these labels and maybe I was too tied to this particular. @SeanOwen My question was more specific than k-means, because I was trying to catalog users in different groups, but I understand how it is possible (it seems too magical) how I can divide them without knowing anything except the TD-IDF matrix and print with that example of related topics. This is why I tied myself to those labels.. I will edit the question. | |
| Dec 30, 2018 at 14:32 | comment | added | Sean Owen | The question doesn't make sense; k-means is an unsupervised technique and by nature has nothing to do with labels | |
| Dec 30, 2018 at 14:32 | history | closed |
OmG Sean Owen |
Needs details or clarity | |
| Dec 30, 2018 at 9:45 | answer | added | Has QUIT--Anony-Mousse | timeline score: 1 | |
| Dec 30, 2018 at 9:35 | review | Close votes | |||
| Dec 30, 2018 at 14:32 | |||||
| Dec 30, 2018 at 9:20 | history | edited | theantomc | CC BY-SA 4.0 |
added 57 characters in body
|
| Dec 30, 2018 at 9:19 | comment | added | theantomc | the question is...how to apply k-means without the label. I edit the question | |
| Dec 30, 2018 at 9:17 | comment | added | Mark.F | There is no question... | |
| Dec 30, 2018 at 9:15 | review | First posts | |||
| Dec 30, 2018 at 9:17 | |||||
| Dec 30, 2018 at 9:10 | history | asked | theantomc | CC BY-SA 4.0 |