On the stationary state of Kohonen's self-organizing sensory mapping
Biological Cybernetics
Convergence theory for fuzzy c-means: counterexamples and repairs
IEEE Transactions on Systems, Man and Cybernetics
Neural Networks
Topology representing networks
Neural Networks
The dynamic universality of sigmoidal neural networks
Information and Computation
Constructing deterministic finite-state automata in recurrent neural networks
Journal of the ACM (JACM)
Self-organizing maps
The handbook of brain theory and neural networks
Temporal Kohonen Map and the Recurrent Self-Organizing Map: Analytical and Experimental Comparison
Neural Processing Letters
Learning with Recurrent Neural Networks
Learning with Recurrent Neural Networks
Neural Computation and Self-Organizing Maps; An Introduction
Neural Computation and Self-Organizing Maps; An Introduction
Application of Cascade Correlation Networks for Structures toChemistry
Applied Intelligence
Simple Strategies to Encode Tree Automata in Sigmoid Recursive Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Clustering based on conditional distributions in an auxiliary space
Neural Computation
Logo Recognition by Recursive Neural Networks
GREC '97 Selected Papers from the Second International Workshop on Graphics Recognition, Algorithms and Systems
Very Large Two-Level SOM for the Browsing of Newsgroups
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Similarity learning for graph-based image representations
Pattern Recognition Letters - Special issue: Graph-based representations in pattern recognition
Hidden Tree Markov Models for Document Image Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
How to make large self-organizing maps for nonvectorial data
Neural Networks - New developments in self-organizing maps
Recursive self-organizing maps
Neural Networks - New developments in self-organizing maps
Generalized relevance learning vector quantization
Neural Networks - New developments in self-organizing maps
Towards Incremental Parsing of Natural Language Using Recursive Neural Networks
Applied Intelligence
A recursive connectionist approach for predicting disulfide connectivity in proteins
Proceedings of the 2003 ACM symposium on Applied computing
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
Neural maps in remote sensing image analysis
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
Spatiotemporal Connectionist Networks: A Taxonomy and Review
Neural Computation
Unsupervised recursive sequence processing
Neurocomputing
Recurrent networks for structured data - A unifying approach and its properties
Cognitive Systems Research
Growing a hypercubical output space in a self-organizing feature map
IEEE Transactions on Neural Networks
Topology preservation in self-organizing feature maps: exact definition and measurement
IEEE Transactions on Neural Networks
Supervised neural networks for the classification of structures
IEEE Transactions on Neural Networks
A general framework for adaptive processing of data structures
IEEE Transactions on Neural Networks
On the implementation of frontier-to-root tree automata in recursive neural networks
IEEE Transactions on Neural Networks
Bankruptcy analysis with self-organizing maps in learning metrics
IEEE Transactions on Neural Networks
Self-organizing maps, vector quantization, and mixture modeling
IEEE Transactions on Neural Networks
A self-organizing map for adaptive processing of structured data
IEEE Transactions on Neural Networks
Introduction: Special issue on neural networks and kernel methods for structured domains
Neural Networks - Special issue on neural networks and kernel methods for structured domains
Dynamics and Topographic Organization of Recursive Self-Organizing Maps
Neural Computation
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
Graph self-organizing maps for cyclic and unbounded graphs
Neurocomputing
Median Topographic Maps for Biomedical Data Sets
Similarity-Based Clustering
Incremental Unsupervised Time Series Analysis Using Merge Growing Neural Gas
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Time Series Clustering for Anomaly Detection Using Competitive Neural Networks
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Gamma SOM for Temporal Sequence Processing
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Classification of graphical data made easy
Neurocomputing
Clustering: A neural network approach
Neural Networks
A Maximum-Likelihood Connectionist Model for Unsupervised Learning over Graphical Domains
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Neurocomputing
Neural gas clustering for dissimilarity data with continuous prototypes
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Data management by self-organizing maps
WCCI'08 Proceedings of the 2008 IEEE world conference on Computational intelligence: research frontiers
Topographic mapping of large dissimilarity data sets
Neural Computation
Visualizing dissimilarity data using generative topographic mapping
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Probabilistic self-organizing maps for qualitative data
Neural Networks
Self-organizing multilayer perceptron
IEEE Transactions on Neural Networks
Relational generative topographic mapping
Neurocomputing
SMO'05 Proceedings of the 5th WSEAS international conference on Simulation, modelling and optimization
Binary tree time adaptive self-organizing map
Neurocomputing
EnvSOM: a SOM algorithm conditioned on the environment for clustering and visualization
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
Gamma-filter self-organizing neural networks for time series analysis
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
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
Recursive self-organizing map as a contractive iterative function system
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Clustering very large dissimilarity data sets
ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition
Information Sciences: an International Journal
Neurocomputing
Essentials of the self-organizing map
Neural Networks
Self-organized reservoirs and their hierarchies
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Business Network Modelling: SOA-Based Approach and Dynamic Logistics Case Study
International Journal of Information System Modeling and Design
An input-output hidden Markov model for tree transductions
Neurocomputing
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Self-organizing models constitute valuable tools for data visualization, clustering, and data mining. Here, we focus on extensions of basic vector-based models by recursive computation in such a way that sequential and tree-structured data can be processed directly. The aim of this article is to give a unified review of important models recently proposed in literature, to investigate fundamental mathematical properties of these models, and to compare the approaches by experiments. We first review several models proposed in literature from a unifying perspective, thereby making use of an underlying general framework which also includes supervised recurrent and recursive models as special cases. We shortly discuss how the models can be related to different neuron lattices. Then, we investigate theoretical properties of the models in detail: we explicitly formalize how structures are internally stored in different context models and which similarity measures are induced by the recursive mapping onto the structures. We assess the representational capabilities of the models, and we shortly discuss the issues of topology preservation and noise tolerance. The models are compared in an experiment with time series data. Finally, we add an experiment for one context model for tree-structured data to demonstrate the capability to process complex structures.