K Means Clustering

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K-means clustering is one of the simplest and popular unsupervised machine learning algorithms. Typically, unsupervised algorithms make inferences from datasets using only input vectors without referring to known, or labelled outcomes.

A cluster refers to a collection of data points aggregated together because of certain similarities.

How the K-means algorithm works

To process the learning data, the K-means algorithm in data mining starts with the first group of randomly selected centroids, which are used as the beginning points for every cluster, and then performs iterative (repetitive) calculations to optimize the positions of the centroids

It halts creating and optimizing clusters when either:

  • The centroids have stabilized — there is no change in their values because the clustering has been successful.
  • The defined number of iterations has been achieved.


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