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Summary
K-means clustering is a method to group data points into specified clusters by measuring distances and calculating means. The process involves selecting a number of clusters (K), assigning points to the nearest cluster, and iterating until clusters stabilize. The elbow method helps determine the optimal K by analyzing variance reduction.