Clustering

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

Subfields and Concepts

 * 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
 * Mixture Models and EM Algorithm
 * Density-based clustering
 * DBSCAN
 * OPTICS
 * Mean-shift
 * Deterministic Annealing
 * Discrete Latent Variable Models
 * Latent Dirichlet Allocation
 * Hidden Markov Models
 * Mixture Models
 * Bayesian Model
 * Non-Bayesian Model

Books and Book Chapters

 * 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.

Software

 * Clustering Analysis (Statistics and Machine Learning Toolbox) - MATLAB

Other Resources

 * Clustering - Notebook