7.3 kmeans demo
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The pre-built demonstration highlights the capabilities of the package.
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K-Means Algorithm Showcase
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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
the Enter key.
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