Getting a bunch of data points and approximating them (in some sense) as membership (possibly fuzzy) in some groups, or regions of feature space.
For certain definitions this can be the same thing as non-negative and/or low rank matrix factorisations if you use mixture models, and is only really different in emphasis from dimensionality reduction. If you start with a list of features then think about “distances” between observations you have just implicitly intuced a weighted graph from your hitherto non-graphy data and are now looking at a networks problem.
If you really care about clustering as such, spectral clustering feels least inelegant if not fastest. Here is Chris Ding’s tutorial on spectral clustering
CONCORinduces a cute similarity measure.
MCL: Markov Cluster Algorithm, a fast and scalable unsupervised cluster algorithm for graphs (also known as networks) based on simulation of (stochastic) flow in graphs.
There are many useful tricks in here, e.g. Belkin and Niyogi (2003) shows how to use a graph Laplacian (possibly a contrived or arbitrary one) to construct “natural” Euclidean coordinates for your data, such that nodes that have much traffic between them in the Laplacian representation have a small Euclidean distance (The “Urban Traffic Planner Fantasy Transformation”) Quickly gives you a similarity measure on really non-Euclidean data. Questions: Under which metrics is it equivalent to multidimensional scaling? Is it worthwhile going the other way and constructing density estimates from induced flow graphs?
Clustering as matrix factorisation
If I know me, I might be looking at this page trying remember which papers situate k-means-type clustering in matrix factorisation literature.
The single-serve paper doing that is Bauckhage (2015), but there are broader versions in (Singh and Gordon 2008; Türkmen 2015), some computer science connections in Mixon, Villar, and Ward (2016), and an older one in Zass and Shashua (2005).
Further things I might discuss here are the graph-flow/Laplacian notions of clustering and the density/centroids approach. I will discuss that under mixture models
Auvolat, Alex, and Pascal Vincent. 2015. “Clustering Is Efficient for Approximate Maximum Inner Product Search.” July 21, 2015. http://arxiv.org/abs/1507.05910.
Bach, Francis R., and Michael I. Jordan. 2006. “Learning Spectral Clustering, with Application to Speech Separation.” Journal of Machine Learning Research 7 (Oct): 1963–2001. http://www.jmlr.org/papers/v7/bach06b.html.
Batson, Joshua, Daniel A. Spielman, and Nikhil Srivastava. 2008. “Twice-Ramanujan Sparsifiers.” August 1, 2008. http://arxiv.org/abs/0808.0163.
Bauckhage, Christian. 2015. “K-Means Clustering Is Matrix Factorization.” December 23, 2015. http://arxiv.org/abs/1512.07548.
Belkin, Mikhail, and Partha Niyogi. 2003. “Laplacian Eigenmaps for Dimensionality Reduction and Data Representation.” Neural Computation 15 (6): 1373–96. https://doi.org/10.1162/089976603321780317.
Clauset, Aaron. 2005. “Finding Local Community Structure in Networks.”
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Ding, C., X. He, and H. Simon. 2005. “On the Equivalence of Nonnegative Matrix Factorization and Spectral Clustering.” In Proceedings of the 2005 SIAM International Conference on Data Mining, 606–10. Proceedings. Society for Industrial and Applied Mathematics. http://ranger.uta.edu/~chqding/papers/NMF-SDM2005.pdf.
Donoho, David L., and Carrie Grimes. 2003. “Hessian Eigenmaps: Locally Linear Embedding Techniques for High-Dimensional Data.” Proceedings of the National Academy of Sciences 100 (10): 5591–6. https://doi.org/10.1073/pnas.1031596100.
Dueck, Delbert, Quaid D. Morris, and Brendan J. Frey. 2005. “Multi-Way Clustering of Microarray Data Using Probabilistic Sparse Matrix Factorization.” Bioinformatics 21 (suppl 1): i144–i151. https://doi.org/10.1093/bioinformatics/bti1041.
Elhamifar, E., and R. Vidal. 2013. “Sparse Subspace Clustering: Algorithm, Theory, and Applications.” IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (11): 2765–81. https://doi.org/10.1109/TPAMI.2013.57.
Fung, Wai Shing, Ramesh Hariharan, Nicholas J. A. Harvey, and Debmalya Panigrahi. 2011. “A General Framework for Graph Sparsification.” In Proceedings of the Forty-Third Annual ACM Symposium on Theory of Computing, 71–80. STOC ’11. New York, NY, USA: ACM. https://doi.org/10.1145/1993636.1993647.
Hallac, David, Jure Leskovec, and Stephen Boyd. 2015. “Network Lasso: Clustering and Optimization in Large Graphs.” July 1, 2015. https://doi.org/10.1145/2783258.2783313.
He, Xiaofei, and Partha Niyogi. 2003. “Locality Preserving Projections.” In Proceedings of the 16th International Conference on Neural Information Processing Systems, 16:153–60. NIPS’03. Cambridge, MA, USA: MIT Press. https://papers.nips.cc/paper/2359-locality-preserving-projections.pdf.
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Masuda, Naoki, Mason A. Porter, and Renaud Lambiotte. 2016. “Random Walks and Diffusion on Networks.” December 10, 2016. http://arxiv.org/abs/1612.03281.
Mixon, Dustin G., Soledad Villar, and Rachel Ward. 2016. “Clustering Subgaussian Mixtures by Semidefinite Programming.” February 21, 2016. http://arxiv.org/abs/1602.06612.
Mohler, George. 2013. “Modeling and Estimation of Multi-Source Clustering in Crime and Security Data.” The Annals of Applied Statistics 7 (3): 1525–39. https://doi.org/10.1214/13-AOAS647.
Newman, Mark E J. 2004. “Detecting Community Structure in Networks.” The European Physical Journal B - Condensed Matter and Complex Systems 38 (2): 321–30. https://doi.org/10.1140/epjb/e2004-00124-y.
Peng, J., and Y. Wei. 2007. “Approximating K‐means‐type Clustering via Semidefinite Programming.” SIAM Journal on Optimization 18 (1): 186–205. https://doi.org/10.1137/050641983.
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———. 2016b. “Randomized Clustered Nystrom for Large-Scale Kernel Machines.” December 19, 2016. http://arxiv.org/abs/1612.06470.
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