Clustering Techniques

By the end, you'll be able to:

  • Distinguish the main families of clustering methods.
  • Choose between hierarchical, partition-based, probabilistic, and density-based clustering.
  • Prepare data for clustering and assess cluster quality critically.
  • Hierarchical clustering and dendrograms
  • K-means and K-medoids
  • Gaussian mixtures and the EM algorithm
  • DBSCAN and OPTICS
  • Choosing the number of clusters
  • Practical tips for scaling, visualization, and validation
Introduction & Hierarchical Clustering