The Self-Organizing Map, or Kohonen Map, is one of the most widely used neural network algorithms, with thousands of applications covered in the literature. It was one of the strong underlying factors in the popularity of neural networks starting in the early 80's. Currently this method has been included in a large number of commercial and public domain software packages. In this book, top experts on the SOM method take a look at the state of the art and the future of this computing paradigm.
The 30 chapters of this book cover the current status of SOM theory, such as connections of SOM to clustering, classification, probabilistic models, and energy functions. Many applications of the SOM are given, with data mining and exploratory data analysis the central topic, applied to large databases of financial data, medical data, free-form text documents, digital images, speech, and process measurements. Biological models related to the SOM are also discussed.
Preface: Kohonen Maps v Table of Contents vii Analyzing and representing multidimensional quantitative and qualitative data: Demographic study of the Rhone valley. The domestic consumption of the Candian Families. 1(14) M. Cottrell P. Gaubert P. Letremy P. Rousset Value maps: Finding value in markets that are expensive 15(18) G. J. Deboeck Data mining and knowledge discovery with emergent Self-Organizing Feature Maps for multivariate time series 33(14) A. Ultsch From aggregation operators to soft Learning Vector Quantization and clustering alogrithms 47(10) N. B. Karayiannis Active learning in Self-Organizing Maps 57(14) M. Hasenjager H. Ritter K. Obermayer Point prototype generation and classifier design 71(26) J. C. Bezdek L. I. Kuncheva Self-Organizing Maps on non-Euclidean spaces 97(14) H. Ritter Self-Organising Maps for pattern recognition 111(10) N. M. Allinson H. Yin Tree structured Self-Organizing Maps 121(10) P. Koikkalainen Growing self-Organizing networks --- History, status quo, and perspectives 131(14) B. Fritzke Kohonen Self-Organizing Map with quantized weights 145(12) P. Thiran On the optimization of Self-organizing Maps by genetic alogrithms 157(14) D. Polani Self organization of a massive text document collection 171(12) T. Kohonen S. Kaski K. Lagus J. Salogarvi J. Honkela V. Paatero A. Saarela Document classification with Self-Organizing Maps 183(14) D. Merkl Navigation in databases using Self-Organising Maps 197(10) S. A. Shumsky A SOM-based sensing approach to robotic manipulation tasks 207(12) E. Cervera A. P. del Pobil SOM-TSP: An approach to optimize surface component mounting on a printed circuit board 219(12) H. Tokutaka K. Fujimura Self-Organising Maps in computer aided design of electronic circuits 231(12) A. Hemani A. Postula Modeling self-organization in the visual cortex 243(10) R. Miikkulainen J. A. Bednar Y. Choe J. Sirosh A spatio-temporal memory based on SOMs with activity diffusion 253(14) N. R. Euliano J. C. Principe Advances in modeling cortical maps 267(12) P. G. Morasso V. Sanguineti F. Frisone Topology preservation in Self-Organizing Maps 279(14) T. Villmann Second-order learning in Self-Organizing Maps 293(10) R. Der M.Herrmann Energy functions for Self-Organizing Maps 303(14) T. Heskes LVQ and single trial EEG classification 317(12) G. Pfurtscheller M. Pregenzer Self-Organizing Map in categorization of voice qualities 329(6) L. Leinonen Chemometric analyses with Self Organising Feature Maps: A worked example of the analysis of cosmetics using Raman spectroscopy 335(14) R. Goodacre N. Kaderbhai A. C. McGovern E. A. Goodacre Self-organizaing Maps for content-Based image database retrieval 349(14) E. Oja J. Laaksonen M. Koskela S. Brandt Indexing audio documents by using latent semantic analysis and SOM 363(12) M. Kurimo Self-Organizing Map in analysis of large-scale industrial systems 375(14) O. Simula J. Ahola E. Alhoniemi J. Himberg J. Vesanto Keyword index 389
Samuel Kaski received the DSc (PhD) degree in Computer Science from Helsinki University of Technology, Finland, in 1997. He is currently a Professor at Aalto University, the Director of Helsinki Institute for Information Technology HIIT, Aalto University and University of Helsinki, Finland, and the Director of Finnish Centre of Excellence in Computational Inference Research COIN. He is an action editor of the Journal of Machine Learning Research, and has chaired several conferences including AISTATS 2014. He has published over 200 peer-reviewed papers and supervised 18 PhD theses. His current research interests include statistical machine learning, computational biology and medicine, information visualization, and exploratory information retrieval.
