Unsupervised methods for grouping observations and assessing whether discovered structure is stable, useful, or meaningful.
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15 articles in this tag.
Clustering always returns groups. Even on random noise, even on data with no structure worth naming, every algorithm will happily partition your points and…
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K-means does not discover the real categories hiding in your data. It draws geometric boundaries around points that happen to sit close together. Those…
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Your clusters looked clean until you changed the random seed. Now the labels shuffled, the boundaries moved, and you are not sure whether you found…
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Every clustering algorithm will happily partition pure noise into tidy groups. Run k-means on random data, and it returns clean circles of assigned points.…
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You learned K-means, and it felt clean: pick a number of groups, let centroids pull points inward, and read off the labels. Then you hit a dataset with…
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Two points sit the same distance from a dense blob. DBSCAN labels one a cluster member and the other noise. That looks arbitrary until you stop asking how…
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Change one number in a DBSCAN call and the whole story of your data can flip: two clusters become one, or a clean grouping dissolves into a field of noise.…
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You can write the Gaussian mixture density in a single line. The trouble starts the moment you take the log.
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Picture a point sitting exactly between two well-separated blobs of data. K-means will force it into one group or the other, even when the data could…
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A hard label is a decision. A posterior probability is a confession about how close that decision was.
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You fit the model, plot a tidy dendrogram, cut it at the biggest gap, and report three clusters. Then you change one argument — the linkage — and get a…
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Change one parameter, get a different tree. Same points, same distance metric, same greedy loop — yet the dendrogram reorganizes itself. That parameter is…
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Most beginners ask the wrong question first. They ask, "Which clustering algorithm is better?" The real question is, "Which question am I actually trying…
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Fit K-means twice on the same data with the same k, and you can get two different clusterings. The second run often reports a lower cost than the first.…
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Most people stop at fit(). They get a label array, scatter-plot it, and call the job done. But the label array is not the result — it is the beginning of…
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