ACM Attributed Graph Clustering for Learning Classes of Images
M. Lozano, und F. Escolano.
Graph Based Representations in Pattern Recognition (2003)

In a previous work we have adapted the Asymmetric Clustering Model (ACM) to the domain of non-attributed graphs. We use our Comb algorithm for graph matching, a population-based method which performs multi-point explorations of the discrete spaceof feasible solutions. Given this algorithm we define an incremental method to obtain a prototypical graph by fusing the elementsof the ensemble weighted by their prior probabilities of belonging to the class. Graph-matching and incremental fusion areintegrated in a EM clustering algorithm.

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http://dx.doi.org/10.1007/3-540-45028-9_22

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@article{keyhere,
  abstract = {In a previous work we have adapted the Asymmetric Clustering Model (ACM) to the domain of non-attributed graphs. We use our
Comb algorithm for graph matching, a population-based method which performs multi-point explorations of the discrete spaceof feasible solutions. Given this algorithm we define an incremental method to obtain a prototypical graph by fusing the elementsof the ensemble weighted by their prior probabilities of belonging to the class. Graph-matching and incremental fusion areintegrated in a EM clustering algorithm.},
  added-at = {2009-04-29T17:55:49.000+0200},
  author = {Lozano, Miguel and Escolano, Francisco},
  biburl = {https://puma.uni-kassel.de/bibtex/2c5b604392e09f7e3fbcbc98f998c7937/hotho},
  description = {SpringerLink - Buchkapitel},
  interhash = {438cc31be12e6e9fd11b2ba2a5cde347},
  intrahash = {c5b604392e09f7e3fbcbc98f998c7937},
  journal = {Graph Based Representations in Pattern Recognition},
  keywords = {clustering toread},
  pages = {247--258},
  timestamp = {2009-04-29T17:55:49.000+0200},
  title = {ACM Attributed Graph Clustering for Learning Classes of Images},
  url = {http://dx.doi.org/10.1007/3-540-45028-9_22},
  year = 2003
}

%0 Journal Article
%1 keyhere
%A Lozano, Miguel
%A Escolano, Francisco
%D 2003
%J Graph Based Representations in Pattern Recognition
%K clustering toread
%P 247--258
%T ACM Attributed Graph Clustering for Learning Classes of Images
%U http://dx.doi.org/10.1007/3-540-45028-9_22
%X In a previous work we have adapted the Asymmetric Clustering Model (ACM) to the domain of non-attributed graphs. We use our
Comb algorithm for graph matching, a population-based method which performs multi-point explorations of the discrete spaceof feasible solutions. Given this algorithm we define an incremental method to obtain a prototypical graph by fusing the elementsof the ensemble weighted by their prior probabilities of belonging to the class. Graph-matching and incremental fusion areintegrated in a EM clustering algorithm.

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ACM Attributed Graph Clustering for Learning Classes of Images
M. Lozano, und F. Escolano.
Graph Based Representations in Pattern Recognition (2003)

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PUMA

ACM Attributed Graph Clustering for Learning Classes of ImagesM. Lozano, und F. Escolano. Graph Based Representations in Pattern Recognition (2003)

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ACM Attributed Graph Clustering for Learning Classes of Images
M. Lozano, und F. Escolano.
Graph Based Representations in Pattern Recognition (2003)