7 K-Means Cluster Analysis

20211228

Package: kmeans (initial version by Gefei Shan, extended and engineered for MLHub by Anita Williams).

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

In addition to the demo command the package also supports train, predict, normalise, and visualise.

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