Flash Points: Discovering Exceptional Pairwise Behaviors in Vote or Rating Data

  • Adnene BelfodilEmail author
  • Sylvie Cazalens
  • Philippe Lamarre
  • Marc Plantevit
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10535)


We address the problem of discovering contexts that lead well-distinguished collections of individuals to change their pairwise agreement w.r.t. their usual one. For instance, in the European parliament, while in overall, a strong disagreement is witnessed between deputies of the far-right French party Front National and deputies of the left party Front de Gauche, a strong agreement is observed between these deputies in votes related to the thematic: External relations with the union. We devise the method DSC (Discovering Similarities Changes) which relies on exceptional model mining to uncover three-set patterns that identify contexts and two collections of individuals where an unexpected strengthening or weakening of pairwise agreement is observed. To efficiently explore the search space, we define some closure operators and pruning techniques using upper bounds on the quality measure. In addition of handling usual attributes (e.g. numerical, nominal), we propose a novel pattern domain which involves hierarchical multi-tag attributes that are present in many datasets. A thorough empirical study on two real-world datasets (i.e., European parliament votes and collaborative movie reviews) demonstrates the efficiency and the effectiveness of our approach as well as the interest and the actionability of the patterns.


Exceptional model mining Subgroup discovery 



This work has been partially supported by the project ContentCheck ANR-15-CE23-0025 funded by the French National Research Agency.


  1. 1.
    Amelio, A., Pizzuti, C.: Analyzing voting behavior in Italian parliament: group cohesion and evolution. In: ASONAM, pp. 140–146. IEEE (2012)Google Scholar
  2. 2.
    Amer-Yahia, S., Kleisarchaki, S., Kolloju, N.K., Lakshmanan, L.V., Zamar, R.H.: Exploring rated datasets with rating maps. In: WWW 2017 (2017)Google Scholar
  3. 3.
    Bendimerad, A.A., Plantevit, M., Robardet, C.: Unsupervised exceptional attributed sub-graph mining in urban data. In: ICDM, pp. 21–30 (2016)Google Scholar
  4. 4.
    Bosc, G., Golebiowski, J., Bensafi, M., Robardet, C., Plantevit, M., Boulicaut, J.-F., Kaytoue, M.: Local subgroup discovery for eliciting and understanding new structure-odor relationships. In: Calders, T., Ceci, M., Malerba, D. (eds.) DS 2016. LNCS (LNAI), vol. 9956, pp. 19–34. Springer, Cham (2016). CrossRefGoogle Scholar
  5. 5.
    Das, M., Amer-Yahia, S., Das, G., Mri, C.Y.: Meaningful interpretations of collaborative ratings. PVLDB 4(11), 1063–1074 (2011)Google Scholar
  6. 6.
    Rebelo de Sá, C., Duivesteijn, W., Soares, C., Knobbe, A.: Exceptional preferences mining. In: Calders, T., Ceci, M., Malerba, D. (eds.) DS 2016. LNCS (LNAI), vol. 9956, pp. 3–18. Springer, Cham (2016). CrossRefGoogle Scholar
  7. 7.
    Duivesteijn, W., Feelders, A.J., Knobbe, A.: Exceptional model mining. Data Mining Knowl. Discov. 30(1), 47–98 (2016)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Duivesteijn, W., Knobbe, A.J., Feelders, A., van Leeuwen, M.: Subgroup discovery meets Bayesian networks - an exceptional model mining approach. In: ICDM 2010 (2010)Google Scholar
  9. 9.
    Etter, V., Herzen, J., Grossglauser, M., Thiran, P.: Mining democracy. ACM (2014)Google Scholar
  10. 10.
    Ganter, B., Kuznetsov, S.O.: Pattern structures and their projections. In: Delugach, H.S., Stumme, G. (eds.) ICCS-ConceptStruct 2001. LNCS (LNAI), vol. 2120, pp. 129–142. Springer, Heidelberg (2001). CrossRefGoogle Scholar
  11. 11.
    Jakulin, A., Buntine, W.: Analyzing the US Senate in 2003: similarities, networks, clusters and blocs (2004)Google Scholar
  12. 12.
    Kaytoue, M., Kuznetsov, S.O., Napoli, A., Duplessis, S.: Mining gene expression data with pattern structures in formal concept analysis. Inf. Sci. 181(10), 1989–2001 (2011)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Kaytoue, M., Plantevit, M., Zimmermann, A., et al.: Exceptional contextual subgraph mining. Mach. Learn. 106, 1171–1211 (2017). MathSciNetCrossRefGoogle Scholar
  14. 14.
    Kralj Novak, P., Lavrač, N., Webb, G.I.: Supervised descriptive rule discovery: a unifying survey of contrast set, emerging pattern and subgroup mining. J. Mach. Learn. Res. 10, 377–403 (2009)zbMATHGoogle Scholar
  15. 15.
    Kuznetsov, S.O.: Learning of simple conceptual graphs from positive and negative examples. In: Żytkow, J.M., Rauch, J. (eds.) PKDD 1999. LNCS (LNAI), vol. 1704, pp. 384–391. Springer, Heidelberg (1999). CrossRefGoogle Scholar
  16. 16.
    Lacy, S., Rosenstiel, T.: Defining and measuring quality journalism (2015)Google Scholar
  17. 17.
    Leman, D., Feelders, A., Knobbe, A.: Exceptional model mining. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008. LNCS (LNAI), vol. 5212, pp. 1–16. Springer, Heidelberg (2008). CrossRefGoogle Scholar
  18. 18.
    Lemmerich, F., Becker, M., Atzmueller, M.: Generic pattern trees for exhaustive exceptional model mining. In: Flach, P.A., De Bie, T., Cristianini, N. (eds.) ECML PKDD 2012. LNCS (LNAI), vol. 7524, pp. 277–292. Springer, Heidelberg (2012). CrossRefGoogle Scholar
  19. 19.
    Omidvar-Tehrani, B., Amer-Yahia, S., Dutot, P.-F., Trystram, D.: Multi-objective group discovery on the social web. In: Frasconi, P., Landwehr, N., Manco, G., Vreeken, J. (eds.) ECML PKDD 2016. LNCS (LNAI), vol. 9851, pp. 296–312. Springer, Cham (2016). CrossRefGoogle Scholar
  20. 20.
    Pajala, A., Jakulin, A., Buntine, W.: Parliamentary group and individual voting behaviour in the finnish parliament in year 2003: a group cohesion and voting similarity analysis (2004)Google Scholar
  21. 21.
    van Leeuwen, M., Knobbe, A.J.: Diverse subgroup set discovery. Data Min. Knowl. Discov. 25(2), 208–242 (2012)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Wrobel, S.: An algorithm for multi-relational discovery of subgroups. In: Komorowski, J., Zytkow, J. (eds.) PKDD 1997. LNCS, vol. 1263, pp. 78–87. Springer, Heidelberg (1997). CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Adnene Belfodil
    • 1
    Email author
  • Sylvie Cazalens
    • 1
  • Philippe Lamarre
    • 1
  • Marc Plantevit
    • 2
  1. 1.INSA Lyon, CNRS, LIRIS UMR 5205LyonFrance
  2. 2.Université Lyon 1, CNRS, LIRIS UMR 5205LyonFrance

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