K-Means Clustering has become the most popular implementation of this approach, shepherding data points into distinctive ‘K Groups’, which may indicate demographic sectors, online communities, or any other possible secret aggregation waiting to be discovered in raw statistical data.
Outside of this application, K-Means Clustering is also employed for landslide prediction, medical image segmentation, image synthesis with GANs, document classification, and city planning, among many other potential and actual uses.
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