@inproceedings{4068,
  title = {Clustering Graphs by Weighted Substructure Mining},
  journal = {Proceedings of the 23rd International Conference on Machine Learning (ICML 2006)},
  booktitle = {ICML 2006},
  abstract = {Graph data is getting increasingly popular in, e.g.,
  bioinformatics and text processing.
  A main difficulty of graph data processing
  lies in the intrinsic high dimensionality of graphs, namely,
  when a graph is represented as a binary feature vector
  of indicators of all possible subgraphs,
  the dimensionality gets too large for usual statistical methods.
  We propose an efficient method for learning
  a binomial mixture model in this feature space.
  Combining the $ell_1$ regularizer and the data structure
  called DFS code tree, the MAP estimate of non-zero parameters
  are computed efficiently by means of the EM algorithm.
  Our method is applied to the clustering of RNA graphs,
  and is compared favorably with graph kernels and
  the spectral graph distance.},
  pages = {953-960},
  editors = {Cohen, W. W., A. Moore},
  publisher = {ACM Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {New York, NY, USA},
  month = jun,
  year = {2006},
  slug = {4068},
  author = {Tsuda, K. and Kudo, T.},
  month_numeric = {6}
}