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This page contains resources about Clustering, Clustering Analysis, Data Clustering and Discrete Latent Variable Models.

Subfields and ConceptsEdit

  • Hierarchical clustering / Connectivity-based clustering
    • Agglomerative
    • Divisive
  • Centroid-based clustering
    • K-means Algorithm / Lloyd's Algorithm
    • Soft K-means Algorithm 
    • Fuzzy c-means
    • K-SVD (used in Dictionary Learning)
  • Distribution-based clustering
  • Density-based clustering
    • DBSCAN
    • OPTICS
    • Mean-shift
  • Deterministic Annealing
  • Discrete Latent Variable Models

Online CoursesEdit

Video LecturesEdit

Lecture NotesEdit

Books and Book ChaptersEdit

  • Duda, R. O., Hart, P. E., & Stork, D. G. (2012). "Chapter 10: Unsupervised Learning and Clustering". Pattern Classification. John Wiley & Sons.
  • Everitt, B. S., Landau, S., Leese, M., & Stahl, D. (2011). Cluster Analysis. 5th Ed. John Wiley & Sons.
  • Alpaydin, E. (2010). "Chapter 7: Clustering". Introduction to machine learning. MIT Press.
  • Theodoridis, S., Pikrakis, A., Koutroumbas, K., & Cavouras, D. (2008). Pattern Recognition. 4th Ed. Academic Press.
  • Kaufman, L., & Rousseeuw, P. J. (2005). Finding groups in data: an introduction to cluster analysis. John Wiley & Sons.
  • MacKay, D. J. (2003). "Chapter 20: Example Inference Task: Clustering". Information Theory, Inference and Learning Algorithms. Cambridge University Press.

Scholarly ArticlesEdit

TutorialsEdit

SoftwareEdit

See alsoEdit

Other ResourcesEdit