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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
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)
Cook, D.J., Holder, L.B.: Substructure discovery using minimum description length and background knowledge. Journal of Artificial Intelligence Research 1, 231–255 (1994)
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)
Dehaspe, L., Toivonen, H.: Discovery of frequent datalog patterns. Data Mining and Knowledge Discovery 3, 7–36 (1999)
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)
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)
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)
Inokuchi, A., Washio, T., Motoda, H.: Complete mining of frequent patterns from graphs: Mining graph data. Machine Learning 50(3), 321–354 (2003)
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)
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)
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)
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)
Nijssen, S., Kok, J.: The gaston tool for frequent subgraph mining. Electronic Notes in Theoretical Computer Science 127, 77–87 (2005)
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 2010
Washio, T., Motoda, H.: State of the art of graph-based data mining. ACM SIGKDD Explorations Newsletter 5, 59–68 (2003)
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)
Yoshida, K., Motoda, H., Indurkhya, N.: Graph-based induction as a unified learning framework. Journal of Applied Intelligence 4, 297–316 (1994)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Hendrickx, T., Cule, B., Meysman, P., Naulaerts, S., Laukens, K., Goethals, B. (2015). Mining Association Rules in Graphs Based on Frequent Cohesive Itemsets. In: Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D., Motoda, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2015. Lecture Notes in Computer Science(), vol 9078. Springer, Cham. https://doi.org/10.1007/978-3-319-18032-8_50
Download citation
DOI: https://doi.org/10.1007/978-3-319-18032-8_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18031-1
Online ISBN: 978-3-319-18032-8
eBook Packages: Computer ScienceComputer Science (R0)