ACM Computing Surveys (CSUR)
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
Graph-Theoretic Techniques for Web Content Mining
Graph-Theoretic Techniques for Web Content Mining
Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)
Bipartite graph matching for computing the edit distance of graphs
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Graph embedding in vector spaces by means of prototype selection
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
When Semi-supervised Learning Meets Ensemble Learning
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
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In the present paper a novel approach to clustering objects given in terms of graphs is introduced. The proposed method is based on an embedding procedure that maps graphs to an n-dimensional real vector space. The basic idea is to view the edit distance of an input graph gto a number of prototype graphs as a vectorial description of g. Based on the embedded graphs, kernel k-means clustering is applied. In several experiments conducted on different graph data sets we demonstrate the robustness and flexibility of our novel graph clustering approach and compare it with a standard clustering procedure directly applied in the domain of graphs.