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Timeline for Benchmarking a clustering algorithm

Current License: CC BY-SA 4.0

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Oct 27, 2022 at 13:37 comment added ttnphns Christian, the point that the mean of a binary variable has a (probabilistic) meaning does not automatically and happily make the dichotomous scale suitable for k-means or other analysis of metric scales: the meaning is an epi-phenomenon, a statistic. I'm just touching the question here and here.
Oct 27, 2022 at 10:59 comment added Christian Hennig @ttnphns Means of binary variables are relative frequencies, therefore estimated probabilities. I think the problem is if we have several dummy variables for categories with originally more than two values, as k-means treats the variables as independent, which they are not in this case.
Oct 20, 2022 at 16:28 comment added ttnphns k-means can work well with binary sequences It could. And in text analysis, as you know, they often apply K-means to binary data normalized (i.e., k-means implied to be done on cosine similarity = on chord distance). The theoretical doubt remains: are we in the right to ever compute centroids directly in the "granular" space such as defined by binary features? Can and when "mean" is a valid concept for categorical data, including binary as categorical?
Oct 20, 2022 at 16:10 history edited Christian Hennig CC BY-SA 4.0
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Oct 20, 2022 at 16:05 history edited ttnphns CC BY-SA 4.0
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Oct 20, 2022 at 16:03 comment added ttnphns Very nice answer. Just to remark, for a reader not well versed, that run multidimensional scaling on them, and then run a GMM on the MDS output implies creating a points x features (euclidean dimensions) data out if a distance matrix, because GMM will call for such data as input.
Oct 20, 2022 at 15:35 comment added Yair M Thanks for the detailed answer! You’ve given me some good HW :) I’ll go over these and perhaps contact you offsite as suggested.
Oct 20, 2022 at 15:34 vote accept Yair M
Oct 19, 2022 at 21:44 history answered Christian Hennig CC BY-SA 4.0