LVQ - Learning Vector Quantization Demonstration

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(Java version is recommended, X11 and Windows versions are deprecated.)

Note: The table package contains some auxiliary programs for preprocessing the data files.

Description

A program to visualize learning vector quantization (a precursor of self-organizing maps) for two-dimensional data.

Full description of this program (included in the source package).

If you have trouble executing the program on Microsoft Windows, check whether you have the Microsoft Visual C++ Redistributable for Visual Studio 2022 (see under "Other Tools and Frameworks") installed, as the program was compiled with Microsoft Visual Studio 2022.

An introduction to neural networks and learning vector quantization in particular can be found in:

• Computational Intelligence — A Methodological Introduction, 3rd edition
  Rudolf Kruse, Sanaz Mostaghim, Christian Borgelt, Christian Braune, and Matthias Steinbrecher
  Springer-Verlag, Berlin/Heidelberg, Germany 2022, ISBN 978-3-030-42227-1
  doi:10.1007/978-3-030-42227-1 springer.com (3rd, extended edition 2022, 653 pages)
• Computational Intelligence - A Methodological Introduction
  Rudolf Kruse, Christian Borgelt, Frank Klawonn, Christian Moewes, Georg Ruß, Matthias Steinbrecher and Pascal Held
  Springer-Verlag, Berlin/Heidelberg, Germany 2013, ISBN 978-1-4471-5012-1
  (488 pages, in English)
• Computational Intelligence
  Rudolf Kruse, Christian Borgelt, Frank Klawonn, Christian Moewes, Georg Ruß, and Matthias Steinbrecher
  Vieweg+Teubner Verlag, Wiesbaden, Germany 2011, ISBN 978-3-8348-1275-9
  (461 pages, in German)
• Neuro-Fuzzy-Systeme - Von den Grundlagen Neuronaler Netze zu modernen Fuzzy-Systemen
  Detlef Nauck, Christian Borgelt, Frank Klawonn und Rudolf Kruse.
  Vieweg-Verlag, Wiesbaden, Germany 2003, ISBN 3-528-25265-0
  (448 pages, in German)