The K-Means Clustering algorithm provides a way to group together similar data points.
This similarity can be measured using any metric but Euclidean Distance is most commonly used.

The algorithm works roughly as follows:

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Initialize the centers of each cluster.

Assign each data point to its closest cluster.

Update the cluster centers according to the new assignments.

Repeat steps 2 and 3 until convergence.

Interactive K-Means clustering.

The above lets you test out a K-Means Classifier. Click to
add new data points and hit 'Cluster' to run the classifier.