7.3 kmeans demo

20211016 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-labeled 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 the Enter key.



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