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This process condenses comprehensive datasets into a more compact set of consultant points. Notably useful in impression and signal processing, k-usually means clustering aids in data reduction by replacing groups of data points with their centroids, thereby preserving the core facts of the first data whilst noticeably reducing the necessary storag