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Computer and Information Science » Artificial Intelligence
Self-Organizing Maps
Edited by George K Matsopoulos, ISBN 978-953-307-074-2, Hard cover, 430 pages, Publisher: InTech, Chapters published April 01, 2010 under CC BY-NC-SA 3.0 license
DOI: 10.5772/3473 
The Self-Organizing Map (SOM) is a neural network algorithm, which uses a competitive learning technique to train itself in an unsupervised manner. SOMs are different from other artificial neural networks in the sense that they use a neighborhood function to preserve the topological properties of the input space and they have been used to create an ordered representation of multi-dimensional data which simplifies complexity and reveals meaningful relationships. Prof. T. Kohonen in the early 1980s first established the relevant theory and explored possible applications of SOMs. Since then, a number of theoretical and practical applications of SOMs have been reported including clustering, prediction, data representation, classification, visualization, etc. This book was prompted by the desire to bring together some of the more recent theoretical and practical developments on SOMs and to provide the background for future developments in promising directions. The book comprises of 25 Chapters which can be categorized into three broad areas: methodology, visualization and practical applications.
- Chapter 1
An Adaptive Fuzzy Neural Network Based on Self-Organizing Map (SOM) - Chapter 2 Learning the Number of Clusters in Self Organizing Map
- Chapter 3 Improvements Quality of Kohonen Maps Using Dimension Reduction Methods
- Chapter 4 PartSOM: A Framework for Distributed Data Clustering Using SOM and K-Means
- Chapter 5 Kohonen Maps Combined to K-means in a Two Level Strategy for Time Series ClusteringApplication to Meteorological and Electricity Load data
- Chapter 6 Visual-Interactive Analysis With Self-Organizing Maps - Advances and Research Challenges
- Chapter 7 Tracking and Visualization of Cluster Dynamics by Sequence-based SOM
- Chapter 8 Visualization with Voronoi Tessellation and Moving Output Units in Self-Organizing Map of the Real-Number System
- Chapter 9 Using Self Organizing Maps for 3D surface and volume adaptive mesh generation
- Chapter 10 Neural-Network Enhanced Visualization of High-Dimensional Data
- Chapter 11 The Self-Organizing Approach for Surface Reconstruction from Unstructured Point Clouds
- Chapter 12 Self-Organizing Maps for Processing of Data with Missing Values and Outliers: Application to Remote Sensing Images
- Chapter 13 Image Clustering and Evaluation on Impact Perforation Test by Self-Organizing Map
- Chapter 14 Self-Organizing Map-based Applications in Remote Sensing
- Chapter 15 Segmentation of Satellite Images Using Self-Organizing Maps
- Chapter 16 Bridging the Semantic Gap using Human Vision System Inspired Features
- Chapter 17 Face Recognition Using Self-Organizing Maps
- Chapter 18 Generation of Emotional Feature Space for Facial Expression Recognition Using Self-Mapping
- Chapter 19 Fingerprint Matching with Self Organizing Maps
- Chapter 20 Multiple Self-Organizing Maps for Control of a Redundant Manipulator with Multiple Cameras
- Chapter 21 Tracking English and Translated Arabic News using GHSOM
- Chapter 22 Self-organizing Maps in Web Mining and Semantic Web
- Chapter 23 Secure Wireless Mesh Network based on Human Immune System and Self-Organizing Map
- Chapter 24 A Knowledge Acquisition Method of Judgment Rules for Spam E-mail by using Self Organizing Map and Automatically Defined Groups by Genetic Programming
- Chapter 25 Applying an SOM Neural Network to Increase the Lifetime of Battery-Operated Wireless Sensor Networks