Central Clustering of Attributed Graphs
Machine Learning
A supervised training algorithm for self-organizing maps for structures
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
Recursive self-organizing network models
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Learning Shape-Classes Using a Mixture of Tree-Unions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamics and Topographic Organization of Recursive Self-Organizing Maps
Neural Computation
Extracting drug utilization knowledge using self-organizing map and rough set theory
Expert Systems with Applications: An International Journal
Probabilistic based recursive model for adaptive processing of data structures
Expert Systems with Applications: An International Journal
Structure clustering for Chinese patent documents
Expert Systems with Applications: An International Journal
A Novel Architecture for the Classification and Visualization of Sequential Data
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Efficient Clustering of Structured Documents Using Graph Self-Organizing Maps
Focused Access to XML Documents
Learning a Generative Model for Structural Representations
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Graph self-organizing maps for cyclic and unbounded graphs
Neurocomputing
Visualization of Structured Data via Generative Probabilistic Modeling
Similarity-Based Clustering
Graph Neural Networks for Object Localization
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Incremental Unsupervised Time Series Analysis Using Merge Growing Neural Gas
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
A Bag of Strings Representation for Image Categorization
Journal of Mathematical Imaging and Vision
The graph neural network model
IEEE Transactions on Neural Networks
Computational capabilities of graph neural networks
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Neurocomputing
Unsupervised recursive sequence processing
Neurocomputing
VISRED: numerical data mining with linear and nonlinear techniques
ICDM'07 Proceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications
IEEE Computational Intelligence Magazine
A machine learning approach to link prediction for interlinked documents
INEX'09 Proceedings of the Focused retrieval and evaluation, and 8th international conference on Initiative for the evaluation of XML retrieval
Supervised encoding of graph-of-graphs for classification and regression problems
INEX'09 Proceedings of the Focused retrieval and evaluation, and 8th international conference on Initiative for the evaluation of XML retrieval
A new tree kernel based on SOM-SD
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
Web spam detection by probability mapping graphSOMs and graph neural networks
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
Computer Vision and Image Understanding
Binary tree time adaptive self-organizing map
Neurocomputing
Processing acyclic data structures using modified self-organizing maps
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Multistrategy self-organizing map learning for classification problems
Computational Intelligence and Neuroscience
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
Clustering XML documents using self-organizing maps for structures
INEX'05 Proceedings of the 4th international conference on Initiative for the Evaluation of XML Retrieval
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
Perspectives of self-adapted self-organizing clustering in organic computing
BioADIT'06 Proceedings of the Second international conference on Biologically Inspired Approaches to Advanced Information Technology
Self-organising map techniques for graph data applications to clustering of XML documents
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Probabilistic based recursive model for face recognition
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
A cascade of unsupervised and supervised neural networks for natural image classification
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Neurocomputing
Autoencoding ground motion data for visualisation
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Protein structural blocks representation and search through unsupervised NN
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Self-Organizing Hidden Markov Model Map (SOHMMM)
Neural Networks
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Recent developments in the area of neural networks produced models capable of dealing with structured data. Here, we propose the first fully unsupervised model, namely an extension of traditional self-organizing maps (SOMs), for the processing of labeled directed acyclic graphs (DAGs). The extension is obtained by using the unfolding procedure adopted in recurrent and recursive neural networks, with the replicated neurons in the unfolded network comprising of a full SOM. This approach enables the discovery of similarities among objects including vectors consisting of numerical data. The capabilities of the model are analyzed in detail by utilizing a relatively large data set taken from an artificial benchmark problem involving visual patterns encoded as labeled DAGs. The experimental results demonstrate clearly that the proposed model is capable of exploiting both information conveyed in the labels attached to each node of the input DAGs and information encoded in the DAG topology.