Clustering on The Standard Errorhttps://jongha-jeon-dev.github.io/standarderror/tags/clustering/Recent content in Clustering on The Standard ErrorHugo -- 0.123.7en-gbWed, 14 Oct 2026 00:00:00 +0000Topology 1: H0 Is Single-Linkage Clustering, Bit for Bithttps://jongha-jeon-dev.github.io/standarderror/lectures/topology-1-h0-single-linkage/Wed, 09 Sep 2026 00:00:00 +0000https://jongha-jeon-dev.github.io/standarderror/lectures/topology-1-h0-single-linkage/The 0-dimensional persistent homology of a Rips filtration returns the same floats as scipy's single-linkage dendrogram — so the barcode is thirty lines of union-find, and it inherits chaining: three points strung between two clusters take the two-cluster signal from 6.70 to 1.00.Topology 2: A Barcode's Numbers Mean Nothing on Their Ownhttps://jongha-jeon-dev.github.io/standarderror/lectures/topology-2-barcode-numbers/Wed, 09 Sep 2026 00:00:00 +0000https://jongha-jeon-dev.github.io/standarderror/lectures/topology-2-barcode-numbers/This episode was written to say that a barcode loses its dynamic range in high dimensions and the display therefore fails. The range does collapse, by a factor of 62 — and the summary becomes more informative, because concentration tightens the null far faster than it shrinks the signal.ML 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 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.