SOM_PAK: The self-organizing map program package. The Self-Organizing Map (SOM) represents the result of a vector quantization algorithm that places a number of reference or codebook vectors into a high-dimensional input data space to approximate to its data sets in an ordered fashion. The SOM PAK program package contains all programs necessary for the correct application of the SelfOrganizing Map algorithm in the visualization of complex experimental data. The first version 1.0 of this program package was published in 1992 and since then the package has been updated regularly to include latest improvements in the SOM implementations. This report that contains the last documentation was prepared for bibliographical purposes.

References in zbMATH (referenced in 23 articles )

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  1. Li, Qianqian; Gu, Jifa: World Expo 2010 pavilions clustering analysis based on self-organizing map (2016)
  2. Hidaka, Tetsuji; Okabe, Yasunori: Detection of changes in non-linear dynamics for time series based on the theory of (\mathrmKM_2 \mathrmO)-Langevin equations (2012)
  3. Liebscher, Steffen; Kirschstein, Thomas; Becker, Claudia: The flood algorithm -- a multivariate, self-organizing-map-based, robust location and covariance estimator (2012)
  4. Hidaka, T.: Detection of change in non-linear dynamics behind the periodic time series and its application to solar cycle prediction (2011)
  5. Deng, Da: Content-based image collection summarization and comparison using self-organizing maps (2007)
  6. Penas, Marta; Penedo, Manuel G.; Carreira, María J.: A neural network based framework for directional primitive extraction (2007) ioport
  7. Bernatavičienẹ, Jolita; Dzemyda, Gintautas; Kurasova, Olga; Marcinkevičius, Virginijus: Optimal decisions in combining the SOM with nonlinear projection methods (2006)
  8. Hynna, Kevin I.; Kaipainen, Mauri: Activation-based recursive self-organising maps: A general formulation and empirical results (2006) ioport
  9. Kim, Sang-Woon; Oommen, B. John: Prototype reduction schemes applicable for non-stationary data sets (2006)
  10. Samsonova, Elena V.; Kok, Joost N.; Ijzerman, Ad P.: TreeSOM: cluster analysis in the self-organizing map (2006)
  11. Wu, Yingxin; Takatsuka, Masahiro: Spherical self-organizing map using efficient indexed geodesic data structure (2006)
  12. Dzemyda, Gintautas: Multidimensional data visualization in the statistical analysis of curricula (2005)
  13. Cinque, L.; Foresti, G.; Lombardi, L.: A clustering fuzzy approach for image segmentation (2004)
  14. Eidenberger, Horst: Statistical analysis of content-based (MPEG-7) descriptors for image retrieval (2004) ioport
  15. Bote-Lorenzo, Miguel L.; Dimitriadis, Yannis A.; Gómez-Sánchez, Eduardo: Automatic extraction of human-recognizable shape and execution prototypes of handwritten characters. (2003) ioport
  16. Dehghan, M.; Faez, K.; Ahmadi, M.; Shridhar, M.: Handwritten Farsi (Arabic) word recognition: a holistic approach using discrete HMM (2001)
  17. Dzemyda, Gintautas: Visualization of a set of parameters characterized by their correlation matrix. (2001)
  18. Blasco, J. A.; Fueyo, N.; Dopazo, C.; Chen, J.-Y.: A self-organizing-map approach to chemistry representation in combustion applications (2000)
  19. Chiang, Jung-Hsien: A hybrid neural network model in handwritten word recognition. (1998) ioport
  20. Chiang, Jung-Hsien: A hybrid neural network model in handwritten word recognition. (1998) ioport

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