0
$\begingroup$

So I have several samples analyzed for their chemical composition. After data analysis, for each sample, I have a list of compounds found and their corresponding relative abundance. Some compounds are unique but most are actually found in most samples.

I want to do clustering analysis based on these list of compounds. How do I go about this? Specifically how to vectorize my dataset since each observation is actually an array with both numerical (abundance) and categorical (compound label) variables.

$\endgroup$

1 Answer 1

-1
$\begingroup$

K-means would be a fine clustering method for you to start with, though you will have to provide the number of clusters you wish for it to return (not sure if you know that/can figure it out). Otherwise check out DBSCAN.

As for the mix of numerical vs categorical data types, all you will need to do is one-hot encoding on your categorical variables. What that does is that it will take all of the known possibilities for a category and it creates new features out of them. A 1 is assigned if the sample is part of that category, and a 0 if it is not. In this way you can use numerical and categorical at the same time, just make sure to normalize your numerical data!

$\endgroup$
4
  • $\begingroup$ Cool. I will try one-hot encoding. I kinda thought of that but I didn't know if it's appropriate (and also what it's called so I can google more about it). Thanks a lot! $\endgroup$
    – quarksome
    Aug 17, 2019 at 1:39
  • $\begingroup$ Ok wait. So for example, I have 10 different compound labels. That translates to 10 new variables (1 or 0). And for each observation, I have a list of compounds with their corresponding abundance. So for each observation, I have an (m x 11) matrix. Can k-means do clustering with matrices as your input? $\endgroup$
    – quarksome
    Aug 17, 2019 at 1:56
  • $\begingroup$ Since you have binary data as features, you will need to do calculations a bit differently for kmeans. Hamming distance should be used instead of the standard euclidean. Otherwise you could do something like hierarchical clustering instead, which I believe works better with binary data. Alternatively, you could just give a feature set of all the different compound labels and their number of occurances, if they are not present then it would just be 0. Then kmeans would work just fine. $\endgroup$ Aug 18, 2019 at 16:16
  • $\begingroup$ Kmeans doesn't optimize Hamming distance. Nor Euclidean actually, only squared Euclidean. It's not really well suited for one-hot encoded data, but trends to produce pretty poor results on those (for a reason: the "mean" in kmeans is a bad idea on such variables). $\endgroup$ Sep 2, 2019 at 21:15

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct.

Not the answer you're looking for? Browse other questions tagged or ask your own question.