Resources for K-Mean Clustering

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.

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Softwares/Codes

• SPAETH2 is a collection of FORTRAN90 routines for analyzing data by grouping into clusters which include KMEANS
• If you are using Java, consider MALLET, an open source software in Java code useful for statistical natural language processing, document classification, clustering, information extraction, and other machine learning applications to text. The code can be downloaded here, and the Kmeans class help is provided in here.
• 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.
• 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).
• K mens in Perl/TK based on this tutorial can be found in here
• Kingsley Tagbo provide VBNET/C# code of k means clustering in his KdKey forum.
• 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. Matlab code of K mean clustering m file can be downloaded here. 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
• 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 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.

Other Tutorials

• 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
• Keith Kintigh has made very good explanation about his program KMEANS that is used for quantitaive tools in Archeology.
• Calculation by hand of k mean clustering algorithm is provided in this tutorial
• Predictive pattern software has another approach to compute k mean clsutering
• Brian T. Luke uses k mean clustering with Evolutionary Programming
• Eric W. Weisstein "K-Means Clustering Algorithm" description is related to Mathematica software
• The MicroCore k-mean clustering tutorial is more related to gens and bioinformatics. It has also hand calculation example.
• Valerie Ohm provide tutorial with implementation for image in C

Downloadable Technical Papers

• 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
• Speeding up k-means Clustering by Bootstrap Averaging (2002) by Ian Davidson and
  Ashwin Satyanarayana
• 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

Applications

• Matt Toews has interesting page of Image Quantization using K mean clustering
• Example of Image segmentation using kmeans function of Mathematica software
• Clustering Ant using Kmean
• Image Colour Segmentationby Genetic Algorithms by Vitorino Ramos, Fernando Muge

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