A conceptual view of clustering

(This collects together posts from an occasional series of essays on clustering: for all posts in this topic, click here)

  1. Clustering: an occasional series
  2. The "I don't like you" view.
  3. $k$-means
  4. Hierarchical methods
  5. Correlation clustering: "I don't like you, but I like them"
  6. Spectral Clustering
  7. An interlude: time-series clustering by Sorelle Friedler.
  8. Mixture models: classification versus clustering
  9. Choosing the number of clusters I: The elbow method
  10. Choosing the number of clusters II: Diminishing returns and the ROC method.
  11. Choosing the number of clusters III: Phase transitions
  12. An interlude: New results on learning mixtures of Gaussians
  13. Clustering as compression
  14. Clustering with outliers (by Sergei Vassilvitskii)
  15. Axioms of clustering (by Sergei Vassilvitskii) 
  16. Large-data clustering Part I: Clusters of clusters

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