I have a small multivariate data ( < 10,000 records) with none of the variable following normal distribution (mix of right-skewed and left-skewed variables). As Gaussian Mixture Model is not suitable for this, what are the other methods that allows computing a probability to belong to each cluster, for every point ?
Fadhaa Ali
. Thanks for answering. I was of assumption that KNN is supervised learning algorithm, used commonly in classification problems. Can you elaborate how to use in situations like clustering with unlabeled dataset ?
Fadhaa Ali
Thanks for sharing the link. The link talks about implementing the KNN as classification problem. My question, however, was how does KNN work for unsupervised learning (clustering).
You might find some joy using CHAID, which produces a tree and leads to forming categories. The independent variables may be a mixture of categorical and continuous variables.
On the other hand, you will not be sent to jail if you use Linear Discriminant Analysis (LDA). LDS requires all the independent variables to be continuous variables (and, strictly speaking, multivariate normal per subgroup). Note that, if your sample is truly a mixture from different populations, each variable will be the result of mixing different populations, and even if each subpopulation has a normal distribution, the mixture as a whole will not have a normal distribution. Linear relationships among the variables (per subgroup) is probably more important. My advice is: try LDA, and if it produces answers which make sense, go for it.
A third option is to try logistic regression (binary logistic regression if there re two groups, multinomial logistic regression if there are more than two groups). Logistic regression also characterizes the categories and classifies the cases. Logistic regression has the advantage that the independent variables may be a mixture of categorical and continuous variables.
https://people.smp.uq.edu.au/GeoffMcLachlan/lm_smap13.pdf
http://hameddaily.blogspot.com/2015/03/when-not-to-use-gaussian-mixtures-model.html
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