Introduction We propose here a simple, robust and scalable technique to perform supervised clustering on numerical data. It can also be used for density estimation, and even to define a concept of variance that is scale-invariant. This is part of our general statistical framework for data science. Previous articles included in this series are: Model-Free Confidence Intervals Tests of Hypotheses…
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