Encyclopedia of Machine Learning and Data Mining

2017 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Inductive Database Approach to Graphmining

  • Stefan Kramer
Reference work entry
DOI: https://doi.org/10.1007/978-1-4899-7687-1_391


The inductive database approach to graph mining can be characterized by (1) the concept of querying for (subgraph) patterns in databases of graphs, and (2) the use of specific data structures representing the space of solutions. For the former, a query language for the specification of the patterns of interest is necessary. The latter aims at a compact representation of the solution patterns.

Pattern Domain

In contrast to other graph mining approaches, the inductive database approach to graph mining (De Raedt and Kramer 2001; Kramer et al. 2001) focuses on simple patterns (paths and trees) and complex queries (see below), not on complex patterns (general subgraphs) and simple queries (minimum frequency only). While the first approaches were restricted to paths as patterns in graph databases, they were later extended toward unrooted trees (Rückert and Kramer 20032004). Most of the applications are dealing with structures of small molecules and structure–activity...

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Recommended Reading

  1. De Raedt L, Kramer S (2001) The levelwise version space algorithm and its application to molecular fragment finding. In: Proceedings of the seventeenth international joint conference on artificial intelligence (IJCAI 2001). Morgan Kaufmann, San FranciscoGoogle Scholar
  2. De Raedt L, Jaeger M, Lee SD, Mannila H (2002) A theory of inductive query answering. In: Proceedings of the 2002 IEEE international conference on data mining (ICDM 2002). IEEE Computer Society, Washington, DCGoogle Scholar
  3. Fischer J, Heun V, Kramer S (2006) Optimal string mining under frequency constraints. In: Proceedings of the tenth European conference on the principles and practice of knowledge discovery in databases (PKDD 2006). Springer, BerlinGoogle Scholar
  4. Kramer S, De Raedt L, Helma C (2001) Molecular feature mining in HIV data. In: Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining (KDD 2001). ACM, New YorkGoogle Scholar
  5. Lee SD, De Raedt L (2003) An algebra for inductive query evaluation. In: Proceedings of the third IEEE international conference on data mining (ICDM 2003). IEEE Computer Society, Washington, DCGoogle Scholar
  6. Mannila H, Toivonen H (1997) Levelwise search and borders of theories in knowledge discovery. Data Min Knowl Discov 1(3):241–258CrossRefGoogle Scholar
  7. Morishita S, Sese J (2000) Traversing itemset lattice with statistical metric pruning. In: Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on principles of database systems (PODS 2000). ACM, New YorkGoogle Scholar
  8. Rückert U, Kramer S (2003) Generalized version space trees. In: Boulicaut J-F, Dzeroski S (eds) Proceedings of the second international workshop on knowledge discovery in inductive databases (KDID-2003). Berlin, SpringerGoogle Scholar
  9. Rückert U, Kramer S (2004) Frequent free tree discovery in graph data. In: Proceedings of the ACM symposium on applied computing (SAC 2004). ACM, New YorkGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Stefan Kramer
    • 1
  1. 1.Technische Universität MünchenGarching b. MünchenGermany