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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.
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| Title of Computers eBook: Kohonen Maps | |
| Release Date: 07-02-1999 | |
| Publisher: Elsevier Science |
This eBook download is available in the following formats:
| Parent title | Kohonen Maps |
|---|---|
| Encrypted (DRM) | Yes |
| SKU | 9780080535296 |
| File size | 24388 |
| Security | n/a |
| Printing | Not allowed |
| Copying | Not allowed |
| Read aloud | No Sys requirements Download reader |
| Devices | Samsung Tablet, Apple Ipad & Iphone, Barnes & Noble Nook, Kobo eReader, Aluratek Libre, Iliad, Nokia, Blackberry, Hanlin |
| Note | Excellent navigation features are available via Adobe such as bookmarks and a quick access table of contents. Text search is easily accessible. An Adobe DRM-protected file is different than a pdf file in that it uses Adobe DRM (Digital Rights Management) technology, which authors and publishers use to protect their content from illegal online distribution and to set certain privileges such as restrictions on copying and printing. |
Kohonen Maps
Chapter One
Analyzing and representing multidimensional quantitative and qualitative data : Demographic study of the Rhone valley. The domestic consumption of the Canadian families.Marie Cottrell, Patrice Gaubert, Patrick Letremy, Patrick Rousset
SAMOS-MATISSE, Université Paris 1 90, rue de Tolbiac, F-75634 Paris Cedex 13, France
1. INTRODUCTION
The SOM algorithm is now extensively used for data mining, representation of multidimensional data and analysis of relations between variables([1], [2], [5], [9], [11], [12], [13], [15], [16], [17]). With respect to any other classification method, the main characteristic of the SOM classification is the conservation of the topology: after learning, > observations are associated to the same class or to > classes according to the definition of the neighborhood in the SOM network. This feature allows to consider the resulting classification as a good starting point for further developments as shown in what follows.
But in fact its capabilities have not been fully exploited so far. In this chapter, we present some of the techniques that can be derived from the SOM algorithm: the representation of the classes contents, the visualization of the distances between classes, a rapid and robust twolevel classification based on the quantitative variables, the computation of clustering indicators, the crossing of the classification with some qualitative variables to interpret the classification and give prominence to the most important explanatory variables. See in [3], [4], [8], [9] precise definitions of all these techniques. We also define two original algorithms (KORRESP and KACM) to
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