Self-Organising Maps for Hierarchical Tree View Document Clustering Using Contextual Information
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Data visualisation and manifold mapping using the ViSOM
Neural Networks - New developments in self-organizing maps
Advanced visualization of self-organizing maps with vector fields
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Online data visualization using the neural gas network
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Dynamics and Topographic Organization of Recursive Self-Organizing Maps
Neural Computation
Pattern recognition in time series database: A case study on financial database
Expert Systems with Applications: An International Journal
A hybrid SOM-kMER model for data visualization and classification
International Journal of Hybrid Intelligent Systems
Computers in Biology and Medicine
Probabilistic based recursive model for adaptive processing of data structures
Expert Systems with Applications: An International Journal
Case-Based Reasoning Adaptation for High Dimensional Solution Space
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Nonlinear Principal Manifolds --- Adaptive Hybrid Learning Approaches
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Shape recovery by a generalized topology preserving SOM
Neurocomputing
DS '08 Proceedings of the 11th International Conference on Discovery Science
Exploring Topology Preservation of SOMs with a Graph Based Visualization
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Automated Ham Quality Classification Using Ensemble Unsupervised Mapping Models
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Dimension Reduction and Data Visualization Using Neural Networks
Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies
ViSOM for Dimensionality Reduction in Face Recognition
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Probabilistic Self-Organizing Graphs
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Exploiting data topology in visualization and clustering of self-organizing maps
IEEE Transactions on Neural Networks
PolSOM: A new method for multidimensional data visualization
Pattern Recognition
ViSOM ensembles for visualization and classification
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Boosting unsupervised competitive learning ensembles
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Visualization of topology representing networks
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Quality of adaptation of fusion ViSOM
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Nonlinear dimensionality reduction for face recognition
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Adaptive nonlinear manifolds and their applications to pattern recognition
Information Sciences: an International Journal
Graph based representations of density distribution and distances for self-organizing maps
IEEE Transactions on Neural Networks
Self-organizing potential field network: a new optimization algorithm
IEEE Transactions on Neural Networks
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Incremental manifold learning by spectral embedding methods
Pattern Recognition Letters
Topographic mapping of dissimilarity data
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
Analyzing key factors of human resources management
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
On non-markovian topographic organization of receptive fields in recursive self-organizing map
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
A bio-inspired fusion method for data visualization
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
From cluster ensemble to structure ensemble
Information Sciences: an International Journal
Visual query processing for efficient image retrieval using a SOM-based filter-refinement scheme
Information Sciences: an International Journal
On nonlinear dimensionality reduction for face recognition
Image and Vision Computing
A new approach for data clustering and visualization using self-organizing maps
Expert Systems with Applications: An International Journal
Growing Self-Organizing Map with cross insert for mixed-type data clustering
Applied Soft Computing
A self-organizing map for transactional data and the related categorical domain
Applied Soft Computing
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
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When used for visualization of high-dimensional data, the self-organizing map (SOM) requires a coloring scheme, such as the U-matrix, to mark the distances between neurons. Even so, the structures of the data clusters may not be apparent and their shapes are often distorted. In this paper, a visualization-induced SOM (ViSOM) is proposed to overcome these shortcomings. The algorithm constrains and regularizes the inter-neuron distance with a parameter that controls the resolution of the map. The mapping preserves the inter-point distances of the input data on the map as well as the topology. It produces a graded mesh in the data space such that the distances between mapped data points on the map resemble those in the original space, like in the Sammon mapping. However, unlike the Sammon mapping, the ViSOM can accommodate both training data and new arrivals and is much simpler in computational complexity. Several experimental results and comparisons with other methods are presented