Clustering
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This page contains resources about Clustering, Clustering Analysis, Data Clustering and Discrete Latent Variable Models.
Subfields and Concepts[edit]
- 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)
- Agglomerative (i.e. bottom-up approach)
- 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
- Centroid-based Clustering
- 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
Online Courses[edit]
Video Lectures[edit]
Lecture Notes[edit]
Books and Book Chapters[edit]
- 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[edit]
- 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.
Tutorials[edit]
Software[edit]
- Clustering Analysis (Statistics and Machine Learning Toolbox) - MATLAB
- Clustering (scikit-learn) - Python
See also[edit]
- Continuous Latent Variable Models / Dimensionality Reduction, an other set of methods for Unsupervised Learning
- Bayesian Nonparametrics
- Probabilistic Graphical Models
Other Resources[edit]
- Clustering - Notebook
- Clustering - Zoubin Ghahramani
- The 5 Clustering Algorithms Data Scientists Need to Know - blog post
- DP_means - GitHub