Unified Clustering Engine: K-Means → Fuzzy → EM (Poisson & Gaussian) скачать в хорошем качестве

Unified Clustering Engine: K-Means → Fuzzy → EM (Poisson & Gaussian)

Clustering algorithms are often explained in isolation — K-Means here, Fuzzy C-Means there, EM somewhere else with Gaussian examples. In this video we do something different: we show that, from a software engineering point of view, a surprisingly large family of clustering methods can be implemented inside the same algorithmic framework — the same table, the same machine, the same expectation / update loop. We cover: • Hard assignment → K-Means • Soft/fuzzy assignment → Fuzzy C-Means • Probabilistic assignment → Expectation-Maximization (EM) Not in this framework: hierarchical clustering, DBSCAN And: we do *not* restrict EM to Gaussians. We first demonstrate EM on a real-world Poisson mixture in an *insurance context* , before moving to the more familiar (but not the only!) Gaussian case. We also discuss suggestions for robustification (these are not unique nor all-powerful).