The pre-built demonstration highlights the capabilities of the package.
ml demo kmeans
========================== K-Means Algorithm Showcase ========================== K-means is an unsupervised clustering algorithm which does not require any pre-labelled data to build a model. The algorithm groups data into k clusters, each represented by its cluster centroid. The user needs to provide the value of k (the number of clusters). Our first example will build a clustering for a random dataset (a different one each time) consisting of two variables, age and income, for each person. The task begins by randomly choosing k (3) centroids (shown as X's in the graphic). Each point is also coloured according to its nearest centroid. Close the graphic window using Ctrl-W. Press Enter to continue:
The following graphic is displayed by the demo and the demo then waits
for the user to indicate to continue with the demonstration, by typing
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