Kohonen Maps

The book we have at hand is the fourth monograph I wrote for Springer Verlag. The previous one named "Self-Organization and Associative Mem ory" (Springer Series in Information Sciences, Volume 8) came out in 1984. Since then the self-organizing neural-network algorithms called SOM and LVQ have become very popular, as can be seen from the many works re viewed in Chap. 9. The new results obtained in the past ten years or so have warranted a new monograph. Over these years I have also answered lots of questions; they have influenced the contents of the present book. I hope it would be of some interest and help to the readers if I now first very briefly describe the various phases that led to my present SOM research, and the reasons underlying each new step. I became interested in neural networks around 1960, but could not in terrupt my graduate studies in physics. After I was appointed Professor of Electronics in 1965, it still took some years to organize teaching at the uni versity. In 1968 - 69 I was on leave at the University of Washington, and D. Gabor had just published his convolution-correlation model of autoasso ciative memory. I noticed immediately that there was something not quite right about it: the capacity was very poor and the inherent noise and crosstalk were intolerable. In 1970 I therefore sugge~ted the auto associative correlation matrix memory model, at the same time as J.A. Anderson and K. Nakano.

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.

The Self-Organizing Map (SOM), with its variants, is the most popular artificial neural network algorithm in the unsupervised learning category. About 4000 research articles on it have appeared in the open literature, and many industrial projects use the SOM as a tool for solving hard real world problems. Many fields of science have adopted the SOM as a standard analytical tool: statistics, signal processing, control theory, financial analyses, experimental physics, chemistry and medicine. This new edition includes a survey of over 2000 contemporary studies to cover the newest results. Case examples are provided with detailed formulae, illustrations, and tables. Further, a new chapter on software tools for SOM has been included whilst other chapters have been extended and reorganised.

Kohonen Self Organizing Maps (SOM) has found application in practical all fields, especially those which tend to handle high dimensional data. SOM can be used for the clustering of genes in the medical field, the study of multi-media and web based contents and in the transportation industry, just to name a few. Apart from the aforementioned areas this book also covers the study of complex data found in meteorological and remotely sensed images acquired using satellite sensing. Data management and envelopment analysis has also been covered. The application of SOM in mechanical and manufacturing engineering forms another important area of this book. The final section of this book, addresses the design and application of novel variants of SOM algorithms.

The two-volume set LNCS 6593 and 6594 constitutes the refereed proceedings of the 10th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2010, held in Ljubljana, Slovenia, in April 2010. The 83 revised full papers presented were carefully reviewed and selected from a total of 144 submissions. The first volume includes 42 papers and a plenary lecture and is organized in topical sections on neural networks and evolutionary computation.

30 tutorials and more than 100 exercises in chemoinformatics, supported by online software and data sets Chemoinformatics is widely used in both academic and industrial chemical and biochemical research worldwide. Yet, until this unique guide, there were no books offering practical exercises in chemoinformatics methods. Tutorials in Chemoinformatics contains more than 100 exercises in 30 tutorials exploring key topics and methods in the field. It takes an applied approach to the subject with a strong emphasis on problem-solving and computational methodologies. Each tutorial is self-contained and contains exercises for students to work through using a variety of software packages. The majority of the tutorials are divided into three sections devoted to theoretical background, algorithm description and software applications, respectively, with the latter section providing step-by-step software instructions. Throughout, three types of software tools are used: in-house programs developed by the authors, open-source programs and commercial programs which are available for free or at a modest cost to academics. The in-house software and data sets are available on a dedicated companion website. Key topics and methods covered in Tutorials in Chemoinformatics include: Data curation and standardization Development and use of chemical databases Structure encoding by molecular descriptors, text strings and binary fingerprints The design of diverse and focused libraries Chemical data analysis and visualization Structure-property/activity modeling (QSAR/QSPR) Ensemble modeling approaches, including bagging, boosting, stacking and random subspaces 3D pharmacophores modeling and pharmacological profiling using shape analysis Protein-ligand docking Implementation of algorithms in a high-level programming language Tutorials in Chemoinformatics is an ideal supplementary text for advanced undergraduate and graduate courses in chemoinformatics, bioinformatics, computational chemistry, computational biology, medicinal chemistry and biochemistry. It is also a valuable working resource for medicinal chemists, academic researchers and industrial chemists looking to enhance their chemoinformatics skills.

The self-organizing map, first described by the Finnish scientist Teuvo Kohonen, can by applied to a wide range of fields. This book is about such applications, i.e. how the original self-organizing map as well as variants and extensions of it can be applied in different fields. In fourteen chapters, a wide range of such applications is discussed. To name a few, these applications include the analysis of financial stability, the fault diagnosis of plants, the creation of well-composed heterogeneous teams and the application of the self-organizing map to the atmospheric sciences.

This book constitutes the refereed proceedings of the 8th International Workshop on Self-Organizing Maps, WSOM 2011, held in Espoo, Finland, in June 2011. The 36 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on plenaries; financial and societal applications; theory and methodology; applications of data mining and analysis; language processing and document analysis; and visualization and image processing.