**Meet the editor**

Dr. Magnus Johnsson is a researcher from Sweden. He studied mathematics, computer science and cognitive science and received a bachelor of science in computer science in 2003, a master of science in computer science in 2004, a master of arts in cognitive science in 2004 and a PhD in cognitive science in 2009. He is currently a researcher at Lund University in Sweden. His research

interests include artificial neural networks, artificial intelligence, computational cognitive neuroscience, cognitive robotics, and the philosophy of mind. Dr Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance and medicine.

Contents

**Preface VII**

Marina Resta

Pérez

Ryotaro Kamimura

Chapter 1 **Graph Mining Based SOM: A Tool to Analyze Economic Stability 1**

Chapter 2 **Social Interaction and Self-Organizing Maps 27**

Chapter 3 **Using Wavelets for Feature Extraction and Self Organizing**

Chapter 4 **Ex-Post Clustering of Brazilian Beef Cattle Farms Using Soms**

Huliane M. Silva, Cícero A. Silva and Flavius L. Gorgônio

Chapter 6 **Application of Self Organizing Maps to Multi Modal Adaptive Authentication System Using Behavior Biometrics 119**

Chapter 7 **Quantification of Emotions for Facial Expression: Generation of Emotional Feature Space Using Self-Mapping 143** Masaki Ishii, Toshio Shimodate, Yoichi Kageyama, Tsuyoshi

**and Cross-Evaluation Dea Models 67**

Chapter 5 **A Self – Organizing Map Based Strategy for Heterogeneous Teaming 89**

Takahashi and Makoto Nishida

Hiroshi Dozono

**Maps for Fault Diagnosis of Nonlinear Dynamic Systems 43** Héctor Benítez-Pérez, Jorge L. Ortega-Arjona and Alma Benítez-

João Carlos Correia Baptista Soares de Mello, Eliane Gonçalves Gomes, Lidia Angulo Meza, Luiz Biondi Neto, Urbano Gomes Pinto de Abreu, Thiago Bernardino de Carvalho and Sergio de Zen

## Contents

**Preface XI**



Preface

self-organizing map to the atmospheric sciences.

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

> **Dr. Magnus Johnsson** Lund University,

> > Sweden


## Preface

Chapter 8 **A Self Organizing Map Based Motion Classifier with an**

Chapter 9 **Using Self-Organizing Maps to Visualize, Filter and Cluster**

Yuan-Chao Liu, Ming Liu and Xiao-Long Wang

Chapter 11 **Non-Linear Spatial Patterning in Cultural Site Formation**

Roberto Henriques, Victor Lobo and Fernando Bação

Chapter 14 **Image Simplification Using Kohonen Maps: Application to Satellite Data for Cloud Detection and**

Suzanne Angeli, Arnaud Quesney and Lydwine Gross

**Neolithic Tell Site in Greece 221**

Chapter 12 **Spatial Clustering Using Hierarchical SOM 231**

Chapter 13 **Self-Organizing Maps: A Powerful Tool for the Atmospheric Sciences 251** Natasa Skific and Jennifer Francis

**Land Cover Mapping 269**

**Multidimensional Bio-Omics Data 181**

**a Smartphone 161**

**VI** Contents

Ji Zhang and Hai Fang

Dimitris Kontogiorgos

Chapter 10 **Application of Self-Organizing Maps in Text Clustering: A Review 205**

Wattanapong Kurdthongmee

**Extension to Fall Detection Problem and Its Implementation on**

**Processes - The Evidence from Micro-Artefacts in Cores from a**

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.

> **Dr. Magnus Johnsson** Lund University, Sweden

**Chapter 1**

**Graph Mining Based SOM: A Tool to Analyze Economic**

Living in times of Global Financial Crisis (GFC) has offered new challenges to researchers and financial policy makers, in search of tools assuring either to monitor or to prevent the incur‐ rence of critical situations. This issue, as usual, can be managed under various perspectives.

Under the economic profile, two basic strands emerged: various contributions debated on the central role of systemic risk in conditioning countries financial fragility; a second vein concerned the role (either in positive or negative sense) of financial sector on economic growth. Provided the relevance for our work, we will discuss each of them in a deeper way.

For what it concerns the first aspect, there are several definitions of systemic risk (see for in‐ stance: [1]; [2], [3] and [4]), but there is not any widely accepted definition for it. Nevertheless, we agree with the position of [5] who claimed that systemic risk can be identified by the pres‐ ence of two distinct elements: an initial random shock, as the source of systemic impact, and a contagion mechanism (such as the interbank market or the payment system), which spread the negative shock wave to other members of the system. Along this vein, a growing body of em‐ pirical research has already bloomed: [6] suggested a network approach to analyze the impact of liquidity shocks into financial systems; a similar approach was followed by [7] discussing the case of United Kingdom, Boss [8] for Austria, and [9] for Switzerland; more recently Sora‐ maki et. al. (2012) developed a software platform1 that employs graphs models for various pur‐

A second related point concerns the evaluation of how financial sector can condition coun‐ tries' economic growth. There is a general agreement in financial economics literature about

> © 2012 Resta; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

© 2012 Resta; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

distribution, and reproduction in any medium, provided the original work is properly cited.

poses, including to monitor financial contagion spreading effects.

1 Financial Network Analysis (fna): free web version available at: http://www.fna.fi/products/list.

**Stability**

Marina Resta

**1. Introduction**

http://dx.doi.org/10.5772/51240

Additional information is available at the end of the chapter
