Unsupervised-Learning on The Standard Error

Unsupervised-Learning on The Standard Errorhttps://jongha-jeon-dev.github.io/standarderror/tags/unsupervised-learning/Recent content in Unsupervised-Learning on The Standard ErrorHugo -- 0.123.7en-gbWed, 14 Oct 2026 00:00:00 +0000ML 32: k-Means Finds k Pieces Whether or Not There Are Clustershttps://jongha-jeon-dev.github.io/standarderror/lectures/ml-32-kmeans/Tue, 13 Oct 2026 00:00:00 +0000https://jongha-jeon-dev.github.io/standarderror/lectures/ml-32-kmeans/k-means always returns k convex pieces of similar spread, and the silhouette score rates the pieces. On uniform noise it rates them higher than it rates the digits.ML 56: A t-SNE Picture Keeps the Neighbours and Loses the Maphttps://jongha-jeon-dev.github.io/standarderror/lectures/ml-56-tsne/Wed, 14 Oct 2026 00:00:00 +0000https://jongha-jeon-dev.github.io/standarderror/lectures/ml-56-tsne/A cluster ten times wider than another came out the same size, and a gap ten times wider came out about the same width. What survives the picture is who sits next to whom.ML 57: The BIC Counts Gaussians, Not Clustershttps://jongha-jeon-dev.github.io/standarderror/lectures/ml-57-bic/Wed, 14 Oct 2026 00:00:00 +0000https://jongha-jeon-dev.github.io/standarderror/lectures/ml-57-bic/Given one cluster shaped like a square, the BIC asked for more Gaussian components the more data it saw, 18 at 20,000 points. It was answering a question about shape and sample size, not about clusters.ML 58: DBSCAN's Radius Is a Density, and It Gets Only Onehttps://jongha-jeon-dev.github.io/standarderror/lectures/ml-58-dbscan/Wed, 14 Oct 2026 00:00:00 +0000https://jongha-jeon-dev.github.io/standarderror/lectures/ml-58-dbscan/DBSCAN found two interlocking half-moons that k-means could not, at the right radius. When clusters differ in density, no radius is right for all of them, and the usual way of choosing it missed even the moons.ML 59: An Anomaly Score Ranks; the Contamination Setting Only Cutshttps://jongha-jeon-dev.github.io/standarderror/lectures/ml-59-anomaly/Wed, 14 Oct 2026 00:00:00 +0000https://jongha-jeon-dev.github.io/standarderror/lectures/ml-59-anomaly/An isolation forest ranked planted anomalies almost perfectly and then flagged whatever share it was told to. Grouped anomalies and irrelevant features changed the ranking itself.ML 60: The Most Stable k Is Not the Number of Clustershttps://jongha-jeon-dev.github.io/standarderror/lectures/ml-60-stability/Wed, 14 Oct 2026 00:00:00 +0000https://jongha-jeon-dev.github.io/standarderror/lectures/ml-60-stability/Resampling found k-means' two-way split of a single Gaussian as reproducible as three real clusters. Stability measures whether the method has one best answer, and cannot say that there are no clusters.