Self-organizing map for clustering in the graph domain
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
A Comparison of Algorithms for Maximum Common Subgraph on Randomly Connected Graphs
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Spectral Feature Vectors for Graph Clustering
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Similarity learning for graph-based image representations
Pattern Recognition Letters - Special issue: Graph-based representations in pattern recognition
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Area-efficient instruction set synthesis for reconfigurable system-on-chip designs
Proceedings of the 41st annual Design Automation Conference
Central Clustering of Attributed Graphs
Machine Learning
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
Protein classification by matching and clustering surface graphs
Pattern Recognition
Graph embedding using tree edit-union
Pattern Recognition
Case-Based Reasoning for Invoice Analysis and Recognition
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Learning a Generative Model for Structural Representations
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Median graph: A new exact algorithm using a distance based on the maximum common subgraph
Pattern Recognition Letters
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
Median graphs: A genetic approach based on new theoretical properties
Pattern Recognition
Text clustering algorithm based on spectral graph seriation
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Design-space exploration of resource-sharing solutions for custom instruction set extensions
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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
Spectral edit distance method for image clustering
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
From region based image representation to object discovery and recognition
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Indexing tree and subtree by using a structure network
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Deriving common malware behavior through graph clustering
Proceedings of the 6th ACM Symposium on Information, Computer and Communications Security
Computer Vision and Image Understanding
Learning graph prototypes for shape recognition
Computer Vision and Image Understanding
Network ensemble clustering using latent roles
Advances in Data Analysis and Classification
JACKSTRAWS: picking command and control connections from bot traffic
SEC'11 Proceedings of the 20th USENIX conference on Security
Supervised learning of graph structure
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
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
Hypergraph-based image retrieval for graph-based representation
Pattern Recognition
Computer Science Review
Unsupervised clustering of human pose using spectral embedding
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Graph matching and clustering using kernel attributes
Neurocomputing
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Graphs are a powerful and versatile tool useful for representing patterns in various subfields of science and engineering. In many applications, for example, in pattern recognition and computer vision, it is required to measure the similarity of objects for clustering similar patterns. In this paper a new structural method, the Weighted Minimum Common Supergraph (WMCS), for representing a cluster of patterns is proposed. Using this method it becomes easy to extract the common information shared in the patterns of a cluster and separate this information from noise and distortions that usually affect graphs representing real objects. Moreover, experimental results show that WMCS is suitable for performing graph clustering.