How K-Means Clustering Works

K-means clustering is an important tools in data analytics.  It’s actually quite easy once you see it in action.

The K-means algorithm starts by placing K points (centroids) at random locations in space. We then perform the following steps iteratively:

  1. for each instance, we assign it to a cluster with the nearest centroid, and
  2. we move each centroid to the mean of the instances assigned to it.

The algorithm continues until no instances change cluster membership.


You may also like...

1 Response

  1. February 15, 2017

    […] explains how K-means sorts data based on averages.  Useful walk-through of the concept in case my previous post didn’t do it for […]

Leave a Reply

Your email address will not be published. Required fields are marked *