Open Source Clustering Software
2.3K
TL;DR: An improved version of Michael Eisen's well-known Cluster program for Windows, Mac OS X and Linux/Unix is created, and a Python and a Perl interface to the C Clustering Library is generated, thereby combining the flexibility of a scripting language with the speed of C.
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Abstract: SUMMARY\nWe have implemented k-means clustering, hierarchical clustering and self-organizing maps in a single multipurpose open-source library of C routines, callable from other C and C++ programs. Using this library, we have created an improved version of Michael Eisen's well-known Cluster program for Windows, Mac OS X and Linux/Unix. In addition, we generated a Python and a Perl interface to the C Clustering Library, thereby combining the flexibility of a scripting language with the speed of C.\n\n\nAVAILABILITY\nThe C Clustering Library and the corresponding Python C extension module Pycluster were released under the Python License, while the Perl module Algorithm::Cluster was released under the Artistic License. The GUI code Cluster 3.0 for Windows, Macintosh and Linux/Unix, as well as the corresponding command-line program, were released under the same license as the original Cluster code. The complete source code is available at http://bonsai.ims.u-tokyo.ac.jp/mdehoon/software/cluster. Alternatively, Algorithm::Cluster can be downloaded from CPAN, while Pycluster is also available as part of the Biopython distribution.
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References
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Teuvo Kohonen
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TL;DR: The Self-Organising Map (SOM) algorithm was introduced by the author in 1981 as mentioned in this paper, and many applications form one of the major approaches to the contemporary artificial neural networks field, and new technologies have already been based on it.
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Teuvo Kohonen
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TL;DR: The self-organizing map, an architecture suggested for artificial neural networks, is explained by presenting simulation experiments and practical applications, and an algorithm which order responses spatially is reviewed, focusing on best matching cell selection and adaptation of the weight vectors.
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Probability Theory: The Logic of Science/The Fundamentals of Risk Measurement/The Elements of Statistical Learning: Data Mining, Inference and Prediction
TL;DR: The Elements of Statistical LearningAn Introduction to Statistical LearningPattern Recognition and Machine LearningData Mining IVStatistics for Machine LearningStatistical Learning for Biomedical DataGeocomputation with RThe Science of Bradley Efron
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Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation
Pablo Tamayo,Donna K. Slonim,Jill P. Mesirov,Qing Zhu,Sutisak Kitareewan,Ethan Dmitrovsky,Eric S. Lander,Todd R. Golub,Todd R. Golub +8 more
TL;DR: In this article, the application of self-organizing maps, a type of mathematical cluster analysis that is particularly well suited for recognizing and classifying features in complex, multidi-mensional data, is described.