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What is K-Means and EM Algorithm Explained about?
The k-means algorithm clusters data points into k groups by iteratively updating cluster centroids. It assigns points to the nearest centroid and recalculates centroids as the mean of points in each cluster. The EM algorithm finds maximum likelihood parameters for a model with latent variables. It treats the latent variables as missing data and iterates between an expectation (E) step, where it computes the distribution over latent variables, and a maximization (M) step, where it re-estimates the model pa
- Author
- Hiền Trần
- Language
- EN