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Is there a way to do unsupervised clustering based on similarity (like all other methods), but create clusters by splitting on just one (or specific) features? For example, I have customer data with lots of features, but I want to find the best clustering of data by segmenting the data on specific bands of customer revenue like [\$100, \$1500, \$10000], etc.

I just want to make sure to compare apples to apples in the best possible way. I'm currently thinking a combination of hierarchical clustering (if there's multiple columns) and Random Forest (to split based on specific predictors). I've looked into Unsupervised Random Forest but it seems to just find a best way to cluster data based on similarity by transforming it, and not give how, or which columns they used to split the data.

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If you formulate this as an optimization problem, you can run it through some optimization solver.

I.e. you would define your model with two thresholds t1 < t2 to partition your data into three clusters on your splitting attribute, and the optimization objective is to maximize the average pairwise similarity to all other members of the same partition.

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  • $\begingroup$ Thanks for the input! Do you think this would work via Linear Programming, or it'd require some other form? Asking since I only really know LP. Thanks $\endgroup$
    – Jong Ha Lee
    Commented Jun 26, 2017 at 16:30
  • $\begingroup$ I don't use optimizers, so I can't give you recommendations. Since you know LP, you already know more than I do. $\endgroup$
    – Has QUIT--Anony-Mousse
    Commented Jun 26, 2017 at 18:04

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