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This process condenses comprehensive datasets into a much more compact set of agent factors. Notably useful in picture and signal processing, k-means clustering aids in data reduction by changing groups of data points with their centroids, thereby preserving the Main facts of the initial data whilst drastically lowering the demanded storage Room.[3