Clustering Algorithms Resources

K-means

Yinyang K-Means: A Drop-In Replacement of the Classic K-Means with Consistent Speedup

• paper: http://jmlr.org/proceedings/papers/v37/ding15.html
• github: https://github.com/src-d/kmcuda
• code: http://research.csc.ncsu.edu/nc-caps/yykmeans.tar.bz2

Semi-supervised K-means++

• arxiv: http://arxiv.org/abs/1602.00360

k-Means Clustering Is Matrix Factorization

• arxiv: http://arxiv.org/abs/1512.07548
• note: http://blog.csdn.net/cyh_24/article/details/50408884

An efficient K-means algorithm for Massive Data

• arxiv: http://arxiv.org/abs/1605.02989

Boost K-Means

• arxiv: https://arxiv.org/abs/1610.02483

Compressive K-means

• arxiv: https://arxiv.org/abs/1610.08738

Convergence rate of stochastic k-means

• arxiv: https://arxiv.org/abs/1610.04900
• arxiv: https://arxiv.org/abs/1611.05132

Fast and Provably Good Seedings for k-Means using k-MC^2 and AFK-MC^2

• github: https://github.com/obachem/kmc2
• blog: https://www.infoq.com/news/2016/12/AFK-MC2-boosts-kMeans-seeding

An efficient K -means clustering algorithm for massive data

• keywords: Clustering, massive data, parallelization, unsupervised learning, K-means, K-means++, Mini-batch
• arxiv: https://arxiv.org/abs/1801.02949

Stream Clustering

Neural Network-based Clustering

Spectral Clustering

On Spectral Clustering: Analysis and an algorithm

• intro: NIPS 2001. Andrew Ng
• paper: https://papers.nips.cc/paper/2092-on-spectral-clustering-analysis-and-an-algorithm.pdf
• paper: http://ai.stanford.edu/~ang/papers/nips01-spectral.pdf

Hierarchical Clustering

Online Clustering

Papers

On Clustering Validation Techniques (2001)

• intro: “This paper introduces the fundamental concepts of clustering while it surveys the widely known clustering algorithms in a comparative way”
• paper: http://web.itu.edu.tr/sgunduz/courses/verimaden/paper/validity_survey.pdf

Stream Clustering

• slides: www.cse.msu.edu/~cse902/S14/ppt/Stream%20Clustering.pptx

Neural network-based clustering using pairwise constraints

• arxiv: http://arxiv.org/abs/1511.06321
• homepage: http://yenchanghsu.github.io/NNclustering/
• github: https://github.com/yenchanghsu/NNclustering

PAC-Bayesian Online Clustering

• arxiv: http://arxiv.org/abs/1602.00522

Compressive Spectral Clustering

• arxiv: http://arxiv.org/abs/1602.02018

Interactive Bayesian Hierarchical Clustering

• arxiv: http://arxiv.org/abs/1602.03258

Practical Introduction to Clustering Data

• arxiv: http://arxiv.org/abs/1602.05124

Rényi divergence minimization based co-regularized multiview clustering

• paper: http://rd.springer.com.sci-hub.io/article/10.1007/s10994-016-5543-2?wt_mc=internal.event.1.SEM.ArticleAuthorOnlineFirst

Consistent Algorithms for Clustering Time Series

• paper: http://jmlr.org/papers/v17/khaleghi16a.html

Hybridization of Expectation-Maximization and K-Means Algorithms for Better Clustering Performance

• arxiv: http://arxiv.org/abs/1603.07879

mst_clustering: Clustering via Euclidean Minimum Spanning Trees

• paper: http://joss.theoj.org/papers/10.21105/joss.00012
• paper: https://github.com/openjournals/joss-papers/blob/master/joss.00012/10.21105.joss.00012.pdf
• github: https://github.com/jakevdp/mst_clustering

k2-means for fast and accurate large scale clustering

• arxiv: http://arxiv.org/abs/1605.09299

Context Aware Nonnegative Matrix Factorization Clustering

• arxiv: http://arxiv.org/abs/1609.04628

Clustering by fast search and find of density peaks

http://science.sciencemag.org/content/344/6191/1492

• slides: http://conference.mipt.ru/img/conference/material-design-2014/talks/Laio-talk.pdf
• github: https://github.com/thomasp85/densityClust
• github: https://github.com/GuipengLi/Dcluster
• blog: http://eric-yuan.me/clustering-fast-search-find-density-peaks/

Comment on “Clustering by fast search and find of density peaks”

https://arxiv.org/abs/1501.04267

Datasets

Clustering datasets

https://cs.joensuu.fi/sipu/datasets/

Books

• intro: 《Machine Learning with R - Second Edition》by Brett Lantz
• book: https://www.packtpub.com/books/content/introduction-clustering-and-unsupervised-learning

Blogs

Finding the K in K-means by Parametric Bootstrap

• blog: http://www.win-vector.com/blog/2016/02/finding-the-k-in-k-means-by-parametric-bootstrap/

Random walk vectors for clustering

• part I – similarity between objects: http://int8.io/random-walk-vectors-for-clustering-part-i-similarity-between-objects/
• part II – perspective switch: http://int8.io/random-walk-vectors-for-clustering-part-ii-perspective-switch/
• part III: http://int8.io/random-walk-vectors-for-clustering-iii/
• final: http://int8.io/random-walk-vectors-for-clustering-final/

A comparison between PCA and hierarchical clustering

http://www.kdnuggets.com/2016/02/qlucore-comparison-pca-hierarchical-clustering.html

Visualization of Centroid Movements for K-Means Clustering

http://web.cecs.pdx.edu/~lane7/

K-Means Clustering on Handwritten Digits

http://johnloeber.com/docs/kmeans.html

Improved Seeding For Clustering With K-Means++ (★★★★★)

https://datasciencelab.wordpress.com/2014/01/15/improved-seeding-for-clustering-with-k-means/

Spectral Clustering – How Math is Redefining Decision Making

http://www.datasciencecentral.com/profiles/blogs/spectral-clustering-how-math-is-redefining-decision-making?overrideMobileRedirect=1

Visual comparison of machine learning algorithms: Clustering

http://haifengl.github.io/smile/index.html#clustering

Clustering Algorithms: From Start To State Of The Art

https://www.toptal.com/machine-learning/clustering-algorithms

Hierarchical clustering, using it to invest

• blog: http://quantdare.com/2016/06/hierarchical-clustering/

Spectral Clustering: A quick overview

https://charlesmartin14.wordpress.com/2012/10/09/spectral-clustering/

Why K-Means is not always a good idea

https://datasciencemadesimpler.wordpress.com/2016/03/05/why-k-means-is-not-always-a-good-idea/

• youtube: https://www.youtube.com/watch?v=AgPQ76RIi6A&list=PLYx7XA2nY5Gf37zYZMw6OqGFRPjB1jCy6&index=10

Projects

MusicMappr: Find patterns in your favorite songs and remix them on the fly!

• intro: MusicMappr finds chunks of songs that are similar, and clusters them accordingly. You can visualize these clusters and play them back at will. This is for music lovers who are curious about the structures inherent to their favorite songs.
• github: https://github.com/fatsmcgee/MusicMappr

TfKmeans: A implementation of k-means clustering in TensorFlow

• github: http://rasbt.github.io/mlxtend/user_guide/tf_cluster/TfKmeans/

CUDA K-Means Clustering: A CUDA implementation of the k-means clustering algorithm

• homepage: http://serban.org/software/kmeans/
• github: https://github.com/serban/kmeans

kmeans_cuda: CUDA implementation of k-means

• github: https://github.com/phvu/kmeans_cuda

K-means in TensorFlow

• blog: http://nxn.se/post/145634722580/k-means-in-tensorflow
• gist: https://gist.github.com/vals/a01a37b14c4918df7937b30d43327837

VAE-Clustering

• intro: Unsupervised clustering with (Gaussian mixture) VAEs
• github: https://github.com/RuiShu/vae-clustering