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Outline

Title
Abstract
Experimental Settings and Results Discussion
The Case of Germany
The Case of Spain
Theory and Computational Methods
Data Description and Network Architecture
Discussion
Summary of Results
Implication for Automobile Industry
Problems of the Method
Possibilities of the Method
The Case Study
Case Study Results
Case Study
Data Source
Results and Discussion
Educational Data Mining
Metrics for the Evaluation of Results
Used Methodology and Obtained Results
Conclusions and Final Thoughts
Previous Studies
Proposed Method
Result and Discussion
Discussion on Training Data Input Method
Related Work
Brief Introduction to Som
Experimental Results and Discussions
Conclusions and Discission
Background
Conclusions
Som Methodology
Introduction
Methodology
Data Preparation
First Use-Case: Cloud Detection
Data Preparation for Cloud Detection on Modis Data
Second Use Case: Land Cover Mapping
Conclusion
References
All Topics
Computer Science

Applications of Self-Organizing Maps

Profile image of Magnus JohnssonMagnus Johnsson

2012

https://doi.org/10.5772/3464
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304 pages

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1 file

Abstract

The self-organizing map is an unsupervised neural network first described by the Finnish scientist Teuvo Kohonen. By a self-organizing adaptation process this neural network learns to map the input space onto a discretized representation which preserves the topology and reflects the probability distribution of the input. This book is about how the original selforganizing map as well as variants and extensions of it can be applied in different fields. In fourteen chapters, a wide range of applications of the Self-Organizing Map 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.

Related papers

An Analytic investigation into self organizing maps and their network topologies
Renee Baltimore

2010

This paper details master"s thesis work involving research and investigation into the approach of self-organizing maps for clustering of data, more specifically, clustering of image data, and how this can be used in understanding image composition. This work will build upon ideas which have previously been explored, such as using self organizing maps for identifying and grouping different regions of an image which may possess similar features. A large part of this research is based upon experimentation with a variety of topological models of the self-organizing map network and investigation into what advantages these different topologies afford the network in terms of its clustering capabilities.

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A Statistical Tool to Assess the Reliability of Self-Organizing Maps
Marie Cottrell

Advances in Self-Organising Maps, 2001

Making results reliable is one of the major concerns in artificial neural networks research. It is often argued that Self-Organizing Maps are less sensitive than other neural paradigms to problems related to convergence, local minima, etc. This paper introduces objective statistical measures that can be used to assess the stability of the results of SOM, both on the distortion and on the topology preservation points of views.

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2002 Special Issue Statistical tools to assess the reliability of self-organizing maps
Marie Cottrell

2002

Results of neural network learning are always subject to some variability, due to the sensitivity to initial conditions, to convergence to local minima, and, sometimes more dramatically, to sampling variability. This paper presents a set of tools designed to assess the reliability of the results of self-organizing maps (SOM), i.e. to test on a statistical basis the confidence we can have on the result of a specific SOM. The tools concern the quantization error in a SOM, and the neighborhood relations (both at the level of a specific pair of observations and globally on the map). As a by-product, these measures also allow to assess the adequacy of the number of units chosen in a map. The tools may also be used to measure objectively how the SOM are less sensitive to non-linear optimization problems (local minima, convergence, etc.) than other neural network models.

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INVESTIGATION OF SELF-ORGANIZING MAP AND ITS USE IN DATA CLUSTERING
dans dans

2003

Self-Organizing Map (SOM) is a special kind of unsupervised neural network. SOM consists of regular, usually two-dimensional, neurons. During training, SOM forms an elastic net that folds onto the "cloud" formed by the input data. Thus, SOM can be interpreted as a topology preserving mapping from input space onto the twodimensional grid of neurons. In mining data, SOM has been used as a clustering technique. As there are several important issues concerning with data mining clustering techniques, some experiments have been done with the goal of discovering the relation between SOM and the issues. This paper discusses SOM, the experiments and the analytical results of how SOM, in some way, has provided good solutions to several of the issues.

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A self-organizing world: special issue of the 13th edition of the workshop on self-organizing maps and learning vector quantization, clustering and data visualization, WSOM + 2019
Cecilio Angulo

Neural Computing & Applications, 2021

The vibrant Mediterranean city of Barcelona, in Spain, welcomed the 13th edition of WSOM ? 2019, the Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization. It was hosted by the Intelligent Data Science and Artificial Intelligence (IDEAI-UPC) Research Center at Universitat Politècnica de Catalunya (UPC BarcelonaTech) and sponsored by several companies, including Telefónica Alpha (now Kora Health), Amalfi Analytics, LumenAI and PredictAssistance. This was the latest of a series of biannual international conferences that started as WSOM in Helsinki, Finland, back in 1997, under the intellectual inspiration of Prof. Teuvo Kohonen, with a focus on SelfOrganizing Maps. In its 12th edition, it extended its scope to cover the more general domains of unsupervised learning and data visualization. WSOM ? 2019 included talks by renowned keynote speakers including professors Paulo J.G. Lisboa from Liverpool John Moores University, U.K. (Unsupervised...

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The Self Organizing Map as a Tool for Cluster Analysis
Abdu Masanawa Sagir

Menemui Matematik (Discovering Mathematics), 2016

The Self-organizing map is among the most acceptable algorithm in the unsupervised learning technique for cluster analysis. It is an important tool used to map high-dimensional data sets onto a low-dimensional discrete lattice of neurons. This feature is used for clustering and classifying data. Clustering is the process of grouping data elements into classes or clusters so that items in each class or cluster are as similar to each other as possible. In this paper, we present an overview of self organizing map, its architecture, applications and its training algorithm. Computer simulations have been analyzed based on samples of data for clustering problems.

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Statistical tools to assess the reliability of self-organizing maps
Marie Cottrell

Neural Networks, 2002

Michel Verleysen is a Senior Research Fellow of the Belgian National Fund for Scientific Research (FNRS).

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On global self-organizing maps
Wlodzislaw Duch

Proceedings of the" 4th European Symposium on …, 1996

Self-Organizing Feature-Mapping (SOFM) algorithm is frequently used for visualization of high-dimensional (input) data in a lower-dimensional (target) space. This algorithm is based on adaptation of parameters in local neighborhoods and therefore does not lead to the best global visualization of the input space data clusters. SOFM is compared here with alternative methods of global visualization of multidimensional data, such as the multidimensional scaling (MDS) and Sammon non-linear mapping, methods based on minimization of error function measuring topographical distortions. SOFM is inferior as a visualization method but facilitates faster classification. A combination of global methods with SOFM should be useful for visualization and classification.

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Self-organizing neural networks for the analysis and representation of data: Some financial cases
Bonifacio Martín-del-brío

Neural Computing & Applications, 1993

processing. This powerful neural model can implement very complex non-linear mappings, but when outputs are not available or clustering of patterns is required, the use of unsupervised models such as self-organizing maps is more suitable. The present work shows the capabilities of self-organizing feature maps for the analysis and representation of financial data and for aid in financial decisionmaking. For this purpose, we analyse the Spanish banking crisis of 1977of and the Spanish economic situation in 1990 use of this unsupervised model. Emphasis is placed on the analysis of the synaptic weights, fundamental for delimiting regions on the map, such as bankrupt or solvent regions, where similar companies are clustered. The time evolution of the companies and other important conclusions can be drawn from the resulting maps.

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An Intuitive Guide of Self Organizing Maps with Practical Implementation in Minisom
Amir Ali, Muhammad Zain Amin

Self Organizing Maps, 2019

The Self Organizing Map is one of the most popular neural models. It belongs to the category of the competitive learning network. The SOM is based on unsupervised learning, which means that is no human intervention is needed during the training and those little needs to be known about characterized by the input data. We could, for example, use the SOM for clustering membership of the input data. The SOM can be used to detect features inherent to the problem and thus has also been called SOFM the Self Origination Feature Map.

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