I am trying to classify 9 different species of elephants into clusters using unsupervised learning. I have the following data about them:

  • Their height
  • Eye Colour
  • Sound they produce in decibel (dB)

I know there are 9 different elephant species in total. However, I am unsure which algorithm would work better. My dataset size is not too huge, so I was wondering if K-Means would be a general worse option here (as Hierarchical Clustering seem to give the same results on every run unlike K-Means).

Any guidance on this would be helpful, thank you.

  • $\begingroup$ Eye colour is categorical, isn't it? How would you use that with k-means? Actually it isn't quite clear to me how you'd want to use that with hierarchical clustering either. Do you define a distance aggregating the three variables such as Gower's coefficient? $\endgroup$ Oct 24 '20 at 10:27
  • $\begingroup$ Lewian, yes, it categorical. They have some other datetime parameters too :) $\endgroup$
    – fitGirl321
    Oct 25 '20 at 16:49
  • $\begingroup$ There's no Gower's coefficient defined.. $\endgroup$
    – fitGirl321
    Oct 25 '20 at 16:49

If you know the number of clusters already, k-means seems more fit than hierarchical clustering, because it tries to give the best possible solution, while HC uses a greedy algorithm, it doesn't matter much if it is deterministic.

However, k-means and HC are really comparable only if you use Ward method for HC, which is really just a hierarchical version of k-means. Single linkage, for instance, can give much different results.

Also, k-means and HC are not the only alternatives, EM clustering and k-medoids are two famous non hierarchical clustering methods, that provide some variations of k-means, with iterative estimating algorithms more reliable than HC, for a fixed k.

  • $\begingroup$ By Wald you probably mean Ward. "EM clustering" is not a method, you probably mean the EM algorithm for Gaussian mixtures (how to use that with eye colour??), but there are EM-algorithms also for various other models, so the term "EM clustering" doesn't say exactly what method you mean. k-means gives the "best possible solution" only according to its own objective function, which is not necessarily appropriate, particularly if separation between clusters is more important than homogeneity within clusters. $\endgroup$ Oct 24 '20 at 10:30
  • $\begingroup$ this is not a book on clustering, is an answer to the posted question. it's meant to give useful indications, not to confuse the OP, or to give her a master level understanding from scratch. $\endgroup$
    – carlo
    Oct 24 '20 at 10:47
  • $\begingroup$ How do you use Ward with a variable such as eye colour? $\endgroup$ Oct 25 '20 at 9:59
  • $\begingroup$ jsut as the OP would most probably have: one hot encoding. that way a certain value is added to the distance between different eyed beasts, just like with Gower. you are making an issue of nothing. $\endgroup$
    – carlo
    Oct 25 '20 at 10:26
  • $\begingroup$ stackoverflow.com/questions/56171837/… $\endgroup$ Oct 25 '20 at 11:23

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