K Means Clustering

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:

  1. Initialize the centers of each cluster.
  2. Assign each data point to its closest cluster.
  3. Update the cluster centers according to the new assignments.
  4. 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.