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.

 

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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 […]

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