The Supervised Network Self-Organizing Map for Classification of Large Data Sets
Applied Intelligence
Hierarchical Clustering of Document Archives with the Growing Hierarchical Self-Organizing Map
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Clustering of Symbolic Data and Its Validation
AFSS '02 Proceedings of the 2002 AFSS International Conference on Fuzzy Systems. Calcutta: Advances in Soft Computing
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
Clustering and its validation in a symbolic framework
Pattern Recognition Letters
A Dynamic Adaptive Self-Organising Hybrid Model for Text Clustering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Enhancing OLAP functionality using self-organizing neural networks
Neural, Parallel & Scientific Computations - Special issue: Computing intelligence in management
A hybrid neural network based DBMS system for enhanced functionality
Design and application of hybrid intelligent systems
Applying dynamic self organizing maps for identifying changes in data sequences
Design and application of hybrid intelligent systems
Proceedings of the 2005 ACM symposium on Applied computing
Organizing and visualizing software repositories using the growing hierarchical self-organizing map
Proceedings of the 2005 ACM symposium on Applied computing
Adaptive topological tree structure for document organisation and visualisation
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Dynamic self organizing maps for discovery and sharing of knowledge in multi agent systems
Web Intelligence and Agent Systems
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Growing kernel-based self-organized maps trained with supervised bias
Intelligent Data Analysis
A new approach to hierarchical clustering and structuring of data with Self-Organizing Maps
Intelligent Data Analysis
3D head model retrieval in kernel feature space using HSOM
Pattern Recognition
ICCOMP'05 Proceedings of the 9th WSEAS International Conference on Computers
A Structural Adapting Self-organizing Maps Neural Network
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
A method for multilingual text mining and retrieval using growing hierarchical self-organizing maps
Journal of Information Science
Incremental board: a grid-based space for visualizing dynamic data sets
Proceedings of the 2009 ACM symposium on Applied Computing
A granular computing framework for self-organizing maps
Neurocomputing
Self-adaptive neural networks based on a Poisson approach for knowledge discovery
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A decade of Kasabov's evolving connectionist systems: a review
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Investigation of Average Mutual Information for Species Separation Using GSOM
FGIT '09 Proceedings of the 1st International Conference on Future Generation Information Technology
PolSOM: A new method for multidimensional data visualization
Pattern Recognition
Tree view self-organisation of web content
Neurocomputing
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Spam detection based on a hierarchical self-organizing map
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Growing competitive network for tracking objects in video sequences
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Network security using growing hierarchical self-organizing maps
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
An incremental space to visualize dynamic data sets
Multimedia Tools and Applications
A new-fangled FES-k-Means clustering algorithm for disease discovery and visual analytics
EURASIP Journal on Bioinformatics and Systems Biology
Future Generation Computer Systems
Clustering high-dimensional data using growing SOM
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Scalable dynamic self-organising maps for mining massive textual data
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Manufacturing yield improvement by clustering
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Lossy image compression using a GHSOM
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Text clustering based on LSA-HGSOM
WISM'11 Proceedings of the 2011 international conference on Web information systems and mining - Volume Part II
Interactive GSOM-Based approaches for improving biomedical pattern discovery and visualization
CIS'04 Proceedings of the First international conference on Computational and Information Science
Clustering massive high dimensional data with dynamic feature maps
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Multimodal feedforward self-organizing maps
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Growing hierarchical principal components analysis self-organizing map
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Representation of procedural knowledge of an intelligent agent using a novel cognitive memory model
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Robust growing hierarchical self organizing map
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Semi-supervised learning of dynamic self-organising maps
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
An extended model on self-organizing map
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
ART-based parallel learning of growing SOMs and its application to TSP
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Scalable data clustering: a sammon's projection based technique for merging GSOMs
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Fast growing self organizing map for text clustering
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Self-organizing maps for translating health care knowledge: a case study in diabetes management
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Thermal modeling of power transformers using evolving fuzzy systems
Engineering Applications of Artificial Intelligence
Self-adaptive and dynamic clustering for online anomaly detection
Expert Systems with Applications: An International Journal
Growing Self-Organizing Map with cross insert for mixed-type data clustering
Applied Soft Computing
Monitoring of complex systems of interacting dynamic systems
Applied Intelligence
Improving performance of self-organising maps with distance metric learning method
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
Investigating individual decision making patterns in games using growing self organizing maps
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Understanding individual play sequences using growing self organizing maps
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
A novel self-adaptive clustering algorithm for dynamic data
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Incremental self-organizing map (iSOM) in categorization of visual objects
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Cartogram visualization for nonlinear manifold learning models
Data Mining and Knowledge Discovery
Probability-based text clustering algorithm by alternately repeating two operations
Journal of Information Science
Hierarchical self-organizing networks for multispectral data visualization
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
Review: A review of novelty detection
Signal Processing
Hierarchical Color Quantization Based on Self-organization
Journal of Mathematical Imaging and Vision
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The growing self-organizing map (GSOM) algorithm is presented in detail and the effect of a spread factor, which can be used to measure and control the spread of the GSOM, is investigated. The spread factor is independent of the dimensionality of the data and as such can be used as a controlling measure for generating maps with different dimensionality, which can then be compared and analyzed with better accuracy. The spread factor is also presented as a method of achieving hierarchical clustering of a data set with the GSOM. Such hierarchical clustering allows the data analyst to identify significant and interesting clusters at a higher level of the hierarchy, and continue with finer clustering of the interesting clusters only. Therefore, only a small map is created in the beginning with a low spread factor, which can be generated for even a very large data set. Further analysis is conducted on selected sections of the data and of smaller volume. Therefore, this method facilitates the analysis of even very large data sets