The video tutorial explains the K-Means clustering algorithm, starting with setting the number of clusters to four. It demonstrates how to train the algorithm on data and predict cluster assignments for new data points. The tutorial also covers the essential requirements for K-Means clustering, including a defined distance metric, the number of clusters, and initial guesses for cluster centroids, emphasizing the partitioning approach used by the algorithm.

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