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 (i.e. bottom-up approach)
 * Minimum Spanning Tree (MST) Clustering Algorithm
 * Single-Linkage Clustering
 * Complete-Linkage Clustering
 * Average-Linkage Clustering
 * Mean or Average Linkage Clustering / UPGMA
 * Centroid Linkage Clustering / UPGMC
 * Minimum Energy Clustering
 * Nearest Neighbour Clustering / Mutual Neighbourhood Clustering
 * Ward's Method
 * Divisive (i.e. top-down approach)
 * Divisive Analysis Clustering (DIANA)
 * Monothetic Analysis (MONA)
 * Partitional Clustering
 * 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 / Clustering by Mixture Decomposition
 * Density-based Clustering
 * DBSCAN
 * OPTICS
 * Mean-shift
 * Graph Theory-based Clustering
 * Search Techniques-based Clustering Algorithms
 * Kernel-based Clustering
 * Sequential Data Clustering
 * Discrete Latent Variable Models
 * Deterministic Annealing
 * 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.
 * Xu, R., & Wunsch, D. (2008). Clustering. John Wiley & Sons.
 * 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 Articles

 * Xu, R., & Wunsch, D. (2005). Survey of clustering algorithms. IEEE Transactions on neural networks, 16(3), 645-678.
 * Jain, A. K., Duin, R. P. W., & Mao, J. (2000). Statistical pattern recognition: A review. IEEE Transactions on pattern analysis and machine intelligence, 22(1), 4-37.
 * Roberts, S. J. (1997). Parametric and non-parametric unsupervised cluster analysis. Pattern Recognition, 30(2), 261-272.

Software

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

Other Resources

 * Clustering - Notebook
 * Clustering - Zoubin Ghahramani
 * The 5 Clustering Algorithms Data Scientists Need to Know - blog post
 * DP_means - GitHub