To identify “natural” groups in a population dataset we can utilise cluster analysis (see the Data Science Survival Guide for details). The k-means clustering algorithm for cluster analysis is an old standard from statistics.
This MLHub package, kmeans, demonstrates k-means cluster analysis and provides a tool to perform k-means cluster analysis on your own data. It uses visualisations and animations to illustrate the iterations of the algorithm over increasingly better fit of clusters to the supplied dataset. We refer to this as training a model to fit the data and to then utilise the model to predict (or assign) a cluster label for each observation in a dataset.
To install, configure, and demonstrate the package:
ml install kmeans ml configure kmeans ml readme kmeans ml commands kmeans ml demo kmeans
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