7 K-Means Cluster Analysis
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.
Your donation will support ongoing availability and give you access to the PDF version of this book. Desktop Survival Guides include Data Science, GNU/Linux, and MLHub. Books available on Amazon include Data Mining with Rattle and Essentials of Data Science. Popular open source software includes rattle, wajig, and mlhub. Hosted by Togaware, a pioneer of free and open source software since 1984. Copyright © 1995-2022 Graham.Williams@togaware.com Creative Commons Attribution-ShareAlike 4.0