tensor + the -arium of aquarium, planetarium: a place where you observe tensors flowing. Playful and distinctive.
Four ways to find groups in unlabeled data. Every page runs live in the browser — click to place points, step the algorithm one move at a time, and see exactly where each one succeeds and where it breaks.
Place the centroids yourself, then alternate two steps: reassign every point to its nearest centroid, move every centroid to the mean of its points. Watch J fall — and watch it settle on the wrong answer when the clusters are not round.
Start with every point its own cluster and merge the closest pair, over and over, until one remains. Compare single, complete and average linkage, then cut the tree by cluster count or by height.
Cluster by density instead of distance to a center. Set ε and minPts, and the algorithm grows clusters of any shape from their core points — and labels whatever is left over as noise.
Soft clustering. Each cluster is an ellipse with its own size and tilt, and each point gets a probability of belonging rather than a label. Step through the E-step and M-step by hand.
Three ways to decide a reading does not belong. Two of them are arithmetic you could do on paper; the third is a model. Each page generates its own data, so press the button a few times and watch how much the answer moves.
How many standard deviations from the mean? Generate a fresh bell curve, pick a cut-off, and see the z-scores. Then add a few odd readings and watch them inflate the very σ you are measuring them against.
The middle half of the data, drawn as a box plot. Quartiles barely move when you add outliers, which is exactly why this rule survives contamination that defeats a z-score.
Cut the plane at random until one point sits alone, and count the cuts. Anomalies are the points that are easy to isolate. Watch a single tree do it, then grow a hundred.