@techreport{5616,
  title = {Frequent Subgraph Retrieval in Geometric Graph Databases},
  abstract = {Discovery of knowledge from geometric graph databases is of particular importance in chemistry and
  biology, because chemical compounds and proteins are represented as graphs with 3D geometric coordinates. In
  such applications, scientists are not interested in the statistics of the whole database. Instead they need information
  about a novel drug candidate or protein at hand, represented as a query graph. We propose a polynomial-delay
  algorithm for geometric frequent subgraph retrieval. It enumerates all subgraphs of a single given query graph
  which are frequent geometric epsilon-subgraphs under the entire class of rigid geometric transformations in a database.
  By using geometric epsilon-subgraphs, we achieve tolerance against variations in geometry. We compare the proposed
  algorithm to gSpan on chemical compound data, and we show that for a given minimum support the total number
  of frequent patterns is substantially limited by requiring geometric matching. Although the computation time per
  pattern is larger than for non-geometric graph mining, the total time is within a reasonable level even for small
  minimum support.},
  number = {180},
  organization = {Max-Planck-Gesellschaft},
  institution = {Max-Planck Institute for Biological Cybernetics, Tübingen, Germany},
  school = {Biologische Kybernetik},
  month = nov,
  year = {2008},
  slug = {5616},
  author = {Nowozin, S. and Tsuda, K.},
  month_numeric = {11}
}