K-means explained in 8 steps: Matt Dancho's beginner-friendly walkthrough of clustering and evaluation
mdancho84 · x · 2026-10-11
Data science educator Matt Dancho ran a step-by-step K-means tutorial thread, calling the algorithm essential but confusing for beginners.
He covers: what K-means is (unsupervised clustering used for customer segmentation, inventory categorization, market segmentation, anomaly detection); its objective of minimizing within-cluster sum of squares (WCSS); the assignment step (Euclidean distance to nearest centroid); the update step (recalculate centroids as cluster means); and iteration until centroids stabilize. For evaluation, he explains Silhouette Score (-1 to 1, measuring how well points fit their own cluster vs neighbors) and the Elbow Method (picking cluster count at the sharp bend in the inertia curve).
He frames it with an industry take: companies now need the "AI Data Scientist" rather than traditional data scientists.
Related event: Matt Dancho Breaks Down K-means Clustering for Beginners(2 posts)→
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