An Image Understanding System Using Attributed Symbolic Representation and Inexact Graph-Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Relaxation by the Hopfield neural network
Pattern Recognition
Organizing Large Structural Modelbases
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters
A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching Hierarchical Structures Using Association Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
On Median Graphs: Properties, Algorithms, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Computer Vision
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Comparing Structures Using a Hopfield-Style Neural Network
Applied Intelligence
Structural Matching in Computer Vision Using Probabilistic Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pairwise Data Clustering by Deterministic Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-organizing map for clustering in the graph domain
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
A novel optimizing network architecture with applications
Neural Computation
Graph clustering using the weighted minimum common supergraph
GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
ACM attributed graph clustering for learning classes of images
GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
A competitive winner-takes-all architecture for classification and pattern recognition of structures
GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
A self-organizing map for adaptive processing of structured data
IEEE Transactions on Neural Networks
Learning Shape-Classes Using a Mixture of Tree-Unions
IEEE Transactions on Pattern Analysis and Machine Intelligence
A spectral approach to learning structural variations in graphs
Pattern Recognition
Graph embedding using tree edit-union
Pattern Recognition
Clustering Using Class Specific Hyper Graphs
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Region and constellations based categorization of images with unsupervised graph learning
Image and Vision Computing
A generative model for graph matching and embedding
Computer Vision and Image Understanding
Finding the k-Most Abnormal Subgraphs from a Single Graph
DS '09 Proceedings of the 12th International Conference on Discovery Science
The Journal of Machine Learning Research
Constellations and the unsupervised learning of graphs
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
IEEE Transactions on Neural Networks
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Large sample statistics in the domain of graphs
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Elkan's k-means algorithm for graphs
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
Computer Vision and Image Understanding
Network ensemble clustering using latent roles
Advances in Data Analysis and Classification
A spectral generative model for graph structure
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Pattern analysis with graphs: Parallel work at Bern and York
Pattern Recognition Letters
A model-based approach to attributed graph clustering
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
On the relation between the common labelling and the median graph
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
A model of analogue K-winners-take-all neural circuit
Neural Networks
Graph matching and clustering using kernel attributes
Neurocomputing
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Partitioning a data set of attributed graphs into clusters arises in different application areas of structural pattern recognition and computer vision. Despite its importance, graph clustering is currently an underdeveloped research area in machine learning due to the lack of theoretical analysis and the high computational cost of measuring structural proximities. To address the first issue, we introduce the concept of metric graph spaces that enables central (or center-based) clustering algorithms to be applied to the domain of attributed graphs. The key idea is to embed attributed graphs into Euclidean space without loss of structural information. In addressing the second issue of computational complexity, we propose a neural network solution of the K-means algorithm for structures (KMS). As a distinguishing feature to improve the computational time, the proposed algorithm classifies the data graphs according to the principle of elimination of competition where the input graph is assigned to the winning model of the competition. In experiments we investigate the behavior and performance of the neural KMS algorithm.