@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.},
  author = {Lozano, Miguel and Escolano, Francisco},
  interhash = {438cc31be12e6e9fd11b2ba2a5cde347},
  intrahash = {c5b604392e09f7e3fbcbc98f998c7937},
  journal = {Graph Based Representations in Pattern Recognition},
  pages = {247--258},
  title = {ACM Attributed Graph Clustering for Learning Classes of Images},
  url = {http://dx.doi.org/10.1007/3-540-45028-9_22},
  year = 2003
}