Resources on K Means
Aside from my tutorial (in Visual Basic Code or in Matlab code ), there are many books and journals or Internet resources discuss about K-mean clustering, your search must be depending on your application. Below are a few list that you may consider. I welcome any feedback and input about other good resources that you want to include in this list; please tell me about it.
- SPAETH2 is a collection of FORTRAN90 routines for analyzing data by grouping into clusters which include KMEANS
- WEKA is another Data mining software under GNU public license in Java which include k means clustering. You may download the source code from Waikato university .
- Biopython project provide tools for computational molecular biology in Python language. The tutorial is in here , which include kMeans clustering module. Other k means code in Python n be found here .
- Tapas Kanungo et al provide C++ code (for Unix, under GPL) and documentation for k-means clustering based on a combination of local search and Lloyd's algorithm (also known as the k-means algorithm).
- David J.C. MacKay give demonstration of using K mean clustering in Octave , a free software similar to Matlab
- Matlab Statistical Toolbox contain a function name kmeans . My own Matlab code is in this page .
- Clustan is special commercial software for clustering.
- XLMiner � and XLStat are both commercial software that support k mean clustering in MS Excel
- Commercial software DTREG for modeling business also provide k means.
- Michael Eisen develop Cluster , an open source clustering software available here implement the most commonly used clustering methods for gene expression data analysis. Available for Mac, Windows and Unix.
- kmeans is also a function in R , a free software for Statistical Computing . More information about k means in R software is also available in here
- If you are using SPSS , it is also called quick clustering, more information is available in here
- Fuzzy c mean clustering in Matlab can be downloaded from Matlab Central File exchange
- Mathematica has kmeans function in the image processing package. You need to load teh package before using it. Clicks here for example and explanation .
- Institute for Signal and Information processing provides Java applet (with source code) of k means, PCA, LDA , SVM etc. Similarly, Jens Spehr and Simon Winkelbach also developed Java applet for k means that useful for teaching interactively.
- Kenichi Kurihara developed Bayesian k means similar to EM algorithm (code in Matlab)
- Cell-kmeans is an open source implementation of K-means algorithm on Cell Broadband Engine written in C
- Analytics1305 is useful public cloud service machine learning technology for extremely large and complex datasets. It contains k means and other clustering techniques
- Accord.NET framework is an extension of A-Forge.NET , provides Machine learning of k-Means. See the tutorial of Cesar Souza on K means implementation in C#
- VLFeat contains C-API implementation of k-means using Lloyd and Elkan algorithm mainly used for Computer Vision
- Jose Fonseca provides k means clustering code in PHP
- Clustering tutorial including K-means, Fuzzy C-means, Hierarchical, Mixture of Gaussians Clustering with Java Applet
- Andrew More provides his lecture slide on k mean and hierarchical clustering
- Philip B. Ender has Multivariate lecture note , which include k mean clustering in Stata 7
- Brian T. Luke uses k mean clustering with Evolutionary Programming
- Eric W. Weisstein "K-Means Clustering Algorithm" description is related to Mathematica software
- Valerie Ohm provide tutorial with implementation for image in C
- The Analysis of a Simple k-Means Clustering Algorithm (2000) by Kanungo et al
- An efficient k-means clustering algorithm: Analysis and implementation (2002) by Kanungo et al
- k -Means: A new generalized k-means clustering algorithm (2003) by Yiu-Ming Cheung
up k-means Clustering by Bootstrap Averaging
(2002) by Ian Davidson
- Constrained K-means Clustering with Background Knowledge (2001) by Wagstaff et al
- Visualizing non-hierarchical and hierarchical cluster analyses with clustergrams by Matthias Schonlau
- K-Means Clustering Over a Large, Dynamic Network (2006) by Souptik Datta, Chris Giannella, and Hillol Kargupta in Proceedings of the 2006 SIAM International Conference on Data Mining
- Bayesian K-Means as a "Maximization-Expectation" Algorithm ,(2006) by Max Welling and Kenichi Kurihara
- Pierre Legendre developed K-Means least-squares partitioning method.
- Dhillon, Guan and Kulis linkage a theoretical connection between kernel k means and spectral clustering
- Matt Toews has interesting page of Image Quantization using K mean clustering
- K means for clustering malaria