Timeline for Clustering with Latent dirichlet allocation (LDA): Distance Measure
Current License: CC BY-SA 3.0
8 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jul 20, 2017 at 16:02 | vote | accept | Lisa | ||
| Jul 20, 2017 at 14:51 | comment | added | gung - Reinstate Monica | Although this question is based on a lack of understanding about LDA (which is what we're here for), that misunderstanding can be cleared up. The upvoted answer below is an existence proof that it can be addressed. I'm voting to leave open. | |
| Jul 20, 2017 at 14:29 | answer | added | kedarps | timeline score: 9 | |
| Jul 20, 2017 at 9:21 | comment | added | Lisa | From my understanding, exactly thats the reason why LDA holds clustering characteristics. Of course it is no hard clustering like k-Means. If you feed new documents into a trained LDA model, it would give you some topic assignment-probabilities. From those, you then could cluster document similairties...right? Thanks for your comment | |
| Jul 19, 2017 at 18:36 | review | Close votes | |||
| Jul 21, 2017 at 21:17 | |||||
| Jul 19, 2017 at 18:18 | comment | added | Has QUIT--Anony-Mousse | LDA doesn't measure distance, and doesn't do clustering. Every word belongs to every cluster with some (often tiny) probability. So your question is all but clear. | |
| Jul 19, 2017 at 9:43 | review | First posts | |||
| Jul 19, 2017 at 11:52 | |||||
| Jul 19, 2017 at 9:41 | history | asked | Lisa | CC BY-SA 3.0 |