Timeline for How to explore high dimensional data to inform a choice of clustering method?
Current License: CC BY-SA 3.0
5 events
| when toggle format | what | by | license | comment | |
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| Nov 16, 2017 at 21:27 | answer | added | František Kaláb | timeline score: 0 | |
| Nov 15, 2017 at 8:28 | comment | added | František Kaláb | That is a good point to start, does having this assumption required for the mixture of gaussians model really just means that each of my variables have to be Gaussian? Well they're rather not, most of the distributions are left skewed to 0. There's not really a maximum count, but after scaling the data it is of course bounded. | |
| Nov 14, 2017 at 18:31 | comment | added | Pavel Komarov | Counts might be Gaussian if you expect users to perform some action a mean of $\mu$ times with some variance among them of $\sigma^2$. | |
| Nov 14, 2017 at 17:54 | comment | added | gung - Reinstate Monica | If your data are counts, they really can't be Gaussian. They might be something else, though. Is there some kind of maximum possible count (eg, count of heads, where number of coin flips is known)? | |
| Nov 14, 2017 at 17:42 | history | asked | František Kaláb | CC BY-SA 3.0 |