Data Scientist Breaks Down K-means Clustering for Beginners
mdancho84 · x · 2026-08-30
The author runs a beginner-friendly thread demystifying K-means, the unsupervised clustering workhorse used for customer segmentation, inventory categorization, market segmentation, and anomaly detection. It works on unlabeled data to uncover structure, minimizing within-cluster sum of squares (WCSS) by alternating an assignment step (points join the nearest centroid via Euclidean distance) and an update step (centroids become the mean of their cluster points).
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