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Mining Association Rules in Graphs Based on Frequent Cohesive Itemsets

  • Tayena Hendrickx
  • Boris Cule
  • Pieter Meysman
  • Stefan Naulaerts
  • Kris Laukens
  • Bart Goethals
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9078)

Abstract

Searching for patterns in graphs is an active field of data mining. In this context, most work has gone into discovering subgraph patterns, where the task is to find strictly defined frequently re-occurring structures, i.e., node labels always interconnected in the same way. Recently, efforts have been made to relax these strict demands, and to simply look for node labels that frequently occur near each other. In this setting, we propose to mine association rules between such node labels, thus discovering additional information about correlations and interactions between node labels. We present an algorithm that discovers rules that allow us to claim that if a set of labels is encountered in a graph, there is a high probability that some other set of labels can be found nearby. Experiments confirm that our algorithm efficiently finds valuable rules that existing methods fail to discover.

Keywords

Association Rule Mining Association Rule Node Label Nucleic Acid Research Frequent Subgraph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Abdulrehman, D., Monteiro, P.T., Teixeira, M.C., Mira, N.P., Lourenço, A.B., dos Santos, S.C., Cabrito, T.R., Francisco, A.P., Madeira, S.C., Aires, R.S., Oliveira, A.L., Sá-Correia, I., Freitas, A.T.: YEASTRACT: providing a programmatic access to curated transcriptional regulatory associations in Saccharomyces cerevisiae through a web services interface. Nucleic Acids Research 39 (2011)Google Scholar
  2. 2.
    Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proc. of the ACM SIGMOD Int. Conf. on Managemant of Data, pp. 207–216 (1993)Google Scholar
  3. 3.
    Cook, D.J., Holder, L.B.: Substructure discovery using minimum description length and background knowledge. Journal of Artificial Intelligence Research 1, 231–255 (1994)Google Scholar
  4. 4.
    Cule, B., Goethals, B.: Mining association rules in long sequences. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds.) PAKDD 2010, Part I. LNCS, vol. 6118, pp. 300–309. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  5. 5.
    Dehaspe, L., Toivonen, H.: Discovery of frequent datalog patterns. Data Mining and Knowledge Discovery 3, 7–36 (1999)CrossRefGoogle Scholar
  6. 6.
    Hendrickx, T., Cule, B., Goethals, B.: Mining cohesive itemsets in graphs. In: Džeroski, S., Panov, P., Kocev, D., Todorovski, L. (eds.) DS 2014. LNCS, vol. 8777, pp. 111–122. Springer, Heidelberg (2014) CrossRefGoogle Scholar
  7. 7.
    Hunter, S., Jones, P., Mitchell, A., Apweiler, R., Attwood, T.K., Bateman, A., Bernard, T., Binns, D., Bork, P., Burge, S., de Castro, E., Coggill, P., Corbett, M., Das, U., Daugherty, L., Duquenne, L., Finn, R.D., Fraser, M., Gough, J., Haft, D., Hulo, N., Kahn, D., Kelly, E., Letunic, I., Lonsdale, D., Lopez, R., Madera, M., Maslen, J., McAnulla, C., McDowall, J., McMenamin, C., Mi, H., Mutowo-Muellenet, P., Mulder, N., Natale, D., Orengo, C., Pesseat, S., Punta, M., Quinn, A.F., Rivoire, C., Sangrador-Vegas, A., Selengut, J.D., Sigrist, C.J.A., Scheremetjew, M., Tate, J., Thimmajanarthanan, M., Thomas, P.D., Wu, C.H., Yeats, C., Yong, S.Y.: InterPro in 2011: new developments in the family and domain prediction database. Nucleic Acids Research 40(D1), D306–D312 (2012)CrossRefGoogle Scholar
  8. 8.
    Huntley, R.P., Sawford, T., Mutowo-Meullenet, P., Shypitsyna, A., Bonilla, C., Martin, M.J., O’Donovan, C.: The goa database: gene ontology annotation updates for 2015. Nucleic Acids Research p. gku1113 (2014)Google Scholar
  9. 9.
    Inokuchi, A., Washio, T., Motoda, H.: Complete mining of frequent patterns from graphs: Mining graph data. Machine Learning 50(3), 321–354 (2003)CrossRefMATHGoogle Scholar
  10. 10.
    Inokuchi, A., Washio, T., Motoda, H., Kumasawa, K., Arai, N.: Basket analysis for graph structured data. In: Zhong, N., Zhou, L. (eds.) PAKDD 1999. LNCS (LNAI), vol. 1574, pp. 420–432. Springer, Heidelberg (1999) CrossRefGoogle Scholar
  11. 11.
    Kerrien, S., Aranda, B., Breuza, L., Bridge, A., Broackes-Carter, F., Chen, C., Duesbury, M., Dumousseau, M., Feuermann, M., Hinz, U., Jandrasits, C., Jimenez, R.C., Khadake, J., Mahadevan, U., Masson, P., Pedruzzi, I., Pfeiffenberger, E., Porras, P., Raghunath, A., Roechert, B., Orchard, S., Hermjakob, H.: The IntAct molecular interaction database in 2012. Nucleic Acids Research 40, D841–D846 (2012)CrossRefGoogle Scholar
  12. 12.
    Khan, A., Yan, X., Wu, K.L.: Towards proximity pattern mining in large graphs. In: Proc. of the 2010 ACM SIGMOD Int. Conf. on Management of Data, pp. 867–878 (2010)Google Scholar
  13. 13.
    Licata, L., Briganti, L., Peluso, D., Perfetto, L., Iannuccelli, M., Galeota, E., Sacco, F., Palma, A., Nardozza, A.P., Santonico, E., Castagnoli, L., Cesareni, G.: MINT, the molecular interaction database: 2012 update. Nucleic Acids Research 40(D1), D857–D861 (2012)CrossRefGoogle Scholar
  14. 14.
    Nijssen, S., Kok, J.: The gaston tool for frequent subgraph mining. Electronic Notes in Theoretical Computer Science 127, 77–87 (2005)CrossRefGoogle Scholar
  15. 15.
    Stark, C., Breitkreutz, B.J., Chatr-aryamontri, A., Boucher, L., Oughtred, R., Livstone, M.S., Nixon, J., Auken, K.V., Wang, X., Shi, X., Reguly, T., Rust, J.M., Winter, A., Dolinski, K., Tyers, M.: The BioGRID interaction database: 2011 update. Nucleic Acids Research p. gkq1116, November 2010Google Scholar
  16. 16.
    Washio, T., Motoda, H.: State of the art of graph-based data mining. ACM SIGKDD Explorations Newsletter 5, 59–68 (2003)CrossRefGoogle Scholar
  17. 17.
    Yan, X., Han, J.: gspan: Graph-based substructure pattern mining. In: Proc. of the 2002 IEEE Int. Conf. on Data Mining, pp. 721–724 (2002)Google Scholar
  18. 18.
    Yoshida, K., Motoda, H., Indurkhya, N.: Graph-based induction as a unified learning framework. Journal of Applied Intelligence 4, 297–316 (1994)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Tayena Hendrickx
    • 1
  • Boris Cule
    • 1
  • Pieter Meysman
    • 1
  • Stefan Naulaerts
    • 1
  • Kris Laukens
    • 1
  • Bart Goethals
    • 1
  1. 1.University of AntwerpAntwerpBelgium

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