Advances in Self-Organizing Maps

Name: Advances in Self-Organizing Maps | 8th International Workshop, WSOM 2011, Espoo, Finland, June 13-15, 2011. Proceedings
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8th International Workshop, WSOM 2011, Espoo, Finland, June 13-15, 2011. Proceedings

Jorma Laaksonen Timo Honkela(Herausgeber*in)

Springer (Verlag)

Erschienen am 13. Juni 2011

XII, 368 Seiten

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978-3-642-21566-7 (ISBN)

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Inhalt

Title Page
Preface
Organization
Table of Contents
Plenaries
Topographic Mapping of Dissimilarity Data
Introduction
Topographic Mapping
Median Clustering
Relational Clustering
Efficient Approximations
Conclusions
References
Contextually Self-Organized Maps of Chinese Words
The Method
Preliminary Clustering Results
Effect of Word Frequency on Clustering
General Adjectives
General Nouns
Verbs
General Adverbs
Other Classes
Discussion
References
Financial and Societal Applications
Assessing the Efficiency of Health Care Providers: A SOM Perspective
Introduction
Problem Statement and Data Description
The SOM Algorithm and the Curse of Dimensionality
Simulations and Discussion of the Results
Conclusion
References
Fuzzy Clustering of the Self-Organizing Map: Some Applications on Financial Time Series
Introduction
The Two-Level SOM-FCM Model
Applications of SOM-FCM for Financial Times Series Analysis
The Financial Benchmarking Model
Fuzzy Clustering of the Financial Benchmarking Model
The Currency Crisis Model
Fuzzy Clustering of the Currency Crisis Model
Conclusions
References
Self Organizing Maps as Models of Social Processes: The Case of Electoral Preferences
Introduction
The Model
Results
Conclusions
References
Theory and Methodology
EnvSOM: A SOM Algorithm Conditioned on the Environment for Clustering and Visualization
Introduction
Similar Approaches
The envSOM Algorithm
The First Phase
The Second Phase
Experiments and Results
A Toy Example
O-CN Example
Conclusions
References
Spectral Clustering as an Automated SOM Segmentation Tool
Introduction
Spectral Clustering
Experimental Results
Synthetic Data Sets
Data Sets from UCI Machine Learning Repository
Boston Remote Sensing Data Set
Computational Complexity
Conclusions
References
Sparse Functional Relevance Learning in Generalized Learning Vector Quantization
Introduction
Functional Relevance Learning for GLVQ
Relevance Learning in GLVQ - GRLVQ
Functional Relevance Learning
Sparse GFRLVQ - The S-GFRLVQ Model
Experiments
Conclusion
References
Relevance Learning in Unsupervised Vector Quantization Based on Divergences
Introduction
The Vector Quantization Models
Divergences as Dissimilarity Measure for Spectral Data and Relevance Learning
Experiments
Data
Validity Measures
Experimental Results
Conclusions
References
Requirements for the Learning of Multiple Dynamics
Introduction
Requirements of the Learning Algorithm of Multiple Dynamics
Conventional Methods
Representative Architectures
What Is the Problem?
Natural Algorithm for LAMD
Improvement of Naive Algorithm
RNN-mnSOM and RNNPB with the Natural Algorithm
What Is Required in Learning of Multiple Dynamics?
Conclusion
References
Growing Graph Network Based on an Online Gaussian Mixture Model
Introduction
Framework and Theory
Framework
(1) Online GMM
(2) Generation of Gaussian Kernel Using Information Criterion
(3) Generating and Updating the Path
Algorithm
Simulation
Summary
References
Evolving a Self-Organizing Feature Map for Visual Object Tracking
Introduction
The Proposed Approach
Object Representation
The Evolutionary Self-Organizing Map (EvSOM)
Object Location and Representation Updating
Results and Discussion
Conclusions
References
Topographic Measure Based on External Criteria for Self-Organizing Map
Introduction
Topographic Measure Based on External Criteria
Extension of Set-Based Clustering Measures
Extension of Pairwise-Based Clustering Measures
Neighborhood Function
Properties of the Topographic Measures
Datasets
Experimental Condition
Topographic Component
Effect of Neighborhood Radius
Conclusion
References
Influence of Learning Rates and Neighboring Functions on Self-Organizing Maps
Introduction
Self-Organizing Maps
Principles of Self-Organizing Maps
Neighboring Functions and Learning Rates
Experimental Results
Data Set Analyzed
Dependence of Results on the Learning Parameters
Conclusions
References
Gamma-Filter Self-Organizing Neural Networks for Time Series Analysis
Introduction
Gamma Context Model
?-GNG Algorithm
Experiments
Mackey-Glass Time Series
Bicup 2006 Time Series
Conclusion
References
Applications of Data Mining and Analysis
Self-Organizing Maps of Nutrition, Lifestyle and Health Situation in the World
Introduction
Method and Data
Nutrition, Lifestyle and Health Database
Self-Organizing Maps
Analysis and Results
Nutrition Analysis
Correlation Analysis
Disease Analysis
Conclusion
References
Mining the City Data: Making Sense of Cities with Self-Organizing Maps
Introduction
Urbanism and Urban Data
The Study Case
Discussion and Conclusions
References
A Discussion on Visual Interactive Data Exploration Using Self-Organizing Maps
Introduction
SOM-Based Visualization Techniques
Criteria
Tools for SOM-Based Interactive Data Exploration
Special Purpose Tools
Analytical Purpose Tools
Discussion
Conclusion
References
Design of a Structured 3D SOM as a Music Archive
Introduction
Design of a Structured SOM
Interactive 3D Music Organizer
Measuring the Quality of the 3D Map
Conclusion
References
A Novel Bioinformatics Strategy to Predict Directional Changes of Influenza A Virus Genome Sequences
Introduction
Methods
Results and Discussion
BLSOMs Constructed with almost all Available Virus Sequence
BLSOMs Constructed with Oligonucleotide Composition in Influenza a Virus Sequences
BLSOMs Constructed with Codon Composition
Codons and Oligonucleotides Diagnostic for Host-Specific Separation
Retrospective Time-Series Changes Visualized for Human Viruses
Segments Separately Analyzed
References
Language Processing and Document Analysis
Impairment and Rehabilitation in Bilingual Aphasia: A SOM-Based Model
Introduction
Lexical Access and Bilingual Aphasia
Modeling Approach
Computational Experiments
Discussion and Future Work
References
Gaussian Selection Using Self-Organizing Map for Automatic Speech Recognition
Introduction
Training a SOM of Gaussians
Distance Metric and Neuron Estimation
Map Initialization
VQ Based Gaussian Selection Using SOM
Constructing the Short List of Neuron Selection
Mapping Member Gaussians to Neurons
Experiments
Experiment Settings
The SOM
Experiments Using Other Approaches
Experimental Results
Discussion and Future Work
References
Self Organizing Maps in NLP: Exploration of Coreference Feature Space
Introduction
Data and Features
Data
Features
Interactive Visualization
Applications
Feature Space Exploration
Feature Engineering
Annotation
Conclusion
References
On Wires and Cables: Content Analysis of WikiLeaks Using Self-Organising Maps
Introduction
SOMFramework
Collection
Experimental Analysis
Conclusions
References
Media Map: A Multilingual Document Map with a Design Interface
Introduction
Self-Organizing Map for Information Retrieval
Our Approach
Related Work
Data and Methods
Data
Content Vectors from Multilingual Documents
Document Map Creation
Document Maps
Term Distributions
Class Distributions
Map Interface Design
Conclusions and Discussion
References
A New Label Maximization Based Incremental Neural Clustering Approach: Application to Text Clustering
Introduction
Clustering Quality Evaluation
Various Adaptations of IGNG-F
IGNG-Fv1
IGNG-Fv2 and IGNG-Fv3
Datasets Description
Results
Conclusion
References
A SOM-Based Analysis of Early Prosodic Acquisition of English by Brazilian Learners: Preliminary Results
Introduction
Methods
The Self-Organizing Map
Input Corpus and Data Representation
Speech Signal Parametrization (Feature Extraction)
SOM Training and Data Visualization
Results and Discussion
Conclusion
References
Visualization and Image Processing
A General Framework for Dimensionality Reduction for Large Data Sets
Introduction
Dimensionality Reduction as Cost Optimization and Out-of-Sample Extensions
Dimensionality Reduction for Large Data Sets
Generalization Ability
Conclusion
References
Considering Spatialization Aspects When Positioning Input Vectors on Self-Organizing Map Output Grids
Introduction
Related Works
Outline of the Input Vector Placement Approach
Tests
Discussion of the Results
Conclusion
References
Aircraft Engine Fleet Monitoring Using Self-Organizing Maps and Edit Distance
Introduction
Data
Methodology
Description of the Four Modules
Environmental Conditions Normalization - ECN
Change Detection - CD
Self-Organizing Maps - SOM
Similar Trajectory Matching - SEARCH
Conclusion
References
Classification Using Topologically Preserving Spherical Self-Organizing Maps
Introduction
Algorithm
Application to Iris Benchmark Data Set
Range Distance Calculation
The Conclusions
References
Visualizing Patterns in the Air Quality in Mexico City with Self-Organizing Maps
Introduction
Data Visualization
Results
Discussion and Conclusions
References
Decision of Class Borders on a Spherical SOM with Non-equal Class Distributions
Introduction
Methods and Procedures for Decision Borders on the SOM
Decision Border with Equal Class Distributions
Decision Border with Non-equal Class Distributions
Procedure for Determining Decision Border
Experiments and Results
Dataset for Analysis
Result with Equal Class Distributions
Result with Non-equal Class Distributions
Conclusions
References
Analysing the Structure of Semantic Concepts in Visual Databases
Introduction
Self-Organisation of Semantic Concepts
Experiments
VOC 2007
TRECVID 2010
Conclusions
References
Mapping of the 3D Objects Using Computer Generated Hologram SOM
Introduction
Hologram
Fresnel Hologram
Matched Filtering Using Fresnel Hologram
CGH Self Organizing Map (CGH-SOM)
Experimental Results
Experimental Results Using Artificial Objects
Experimental Results Using Scanned Data
Conclusion
References
Analysing the Similarity of Album Art with Self-Organising Maps
Introduction and Related Work
SOMFramework
Feature Sets
Audio Features
Image Features
Collection
Experimental Analysis
Conclusions
References
Author Index

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