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The K-means algorithm identifies a certain number of centroids within a data set, a centroid being the arithmetic mean of all the data points belonging to a particular cluster. The algorithm then allocates every data point to the nearest cluster as it attempts to keep the clusters as small as possible (the ‘means’ in K-means refers to the task of averaging the data or finding the centroid). At the same time, K-means attempts to keep the other clusters as different as possible.

The K-means algorithm identifies a certain number of centroids within a data set, a centroid being the arithmetic mean of all the data points belonging to a particular cluster. The algorithm then allocates every data point to the nearest cluster as it attempts to keep the clusters as small as possible (the ‘means’ in K-means refers to the task of averaging the data or finding the centroid). At the same time, K-means attempts to keep the other clusters as different as possible.