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 Kmean 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.
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Softwares/Codes
Other Tutorials
Downloadable Technical Papers
Applications
 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 kmeans clustering based on a combination of local search and Lloyd's algorithm (also known as the kmeans 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)
 Cellkmeans is an open source implementation of Kmeans 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 AForge.NET , provides Machine learning of kMeans. See the tutorial of Cesar Souza on K means implementation in C#
 VLFeat contains CAPI implementation of kmeans using Lloyd and Elkan algorithm mainly used for Computer Vision
 Jose Fonseca provides k means clustering code in PHP
 Clustering tutorial including Kmeans, Fuzzy Cmeans, 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 "KMeans Clustering Algorithm" description is related to Mathematica software
 Valerie Ohm provide tutorial with implementation for image in C
 The Analysis of a Simple kMeans Clustering Algorithm (2000) by Kanungo et al
 An efficient kmeans clustering algorithm: Analysis and implementation (2002) by Kanungo et al
 k Means: A new generalized kmeans clustering algorithm (2003) by YiuMing Cheung

Speeding
up kmeans Clustering by Bootstrap Averaging
(2002) by Ian Davidson
and
Ashwin Satyanarayana
 Constrained Kmeans Clustering with Background Knowledge (2001) by Wagstaff et al
 Visualizing nonhierarchical and hierarchical cluster analyses with clustergrams by Matthias Schonlau
 KMeans 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 KMeans as a "MaximizationExpectation" Algorithm ,(2006) by Max Welling and Kenichi Kurihara
 Pierre Legendre developed KMeans leastsquares 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
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