Joint European Conference on Machine Learning and Knowledge Discovery in Databases

ECML PKDD 2015: Machine Learning and Knowledge Discovery in Databases pp 333-336 | Cite as

VIPER – Visual Pattern Explorer

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9286)


We present Viper, for Visual Pattern Explorer, an innovative, browser-based application for interactive pattern exploration, assisted by visualisation, recommendation, and algorithmic search. The target audience consists of domain experts who have access to data but not to –potentially expensive– data mining experts. The goal of the system is to enable the target audience to perform true exploratory data mining. That is, to discover interesting patterns from data, with a focus on subgroup discovery but also facilitating frequent itemset mining.


  1. 1.
    Aggarwal, C., Han, J. (eds.): Frequent Pattern Mining. Springer (2014)Google Scholar
  2. 2.
    Atzmueller, M.: Subgroup discovery. Wiley Interdisc. Rew.: Data Mining and Knowledge Discovery 5(1), 35–49 (2015)Google Scholar
  3. 3.
    Bhuiyan, M., Mukhopadhyay, S., Hasan, M.A.: Interactive pattern mining on hidden data: a sampling-based solution. In: Proc. of CIKM 2012, pp. 95–104 (2012)Google Scholar
  4. 4.
    Boley, M., Mampaey, M., Kang, B., Tokmakov, P., Wrobel, S.: One click mining: Interactive local pattern discovery through implicit preference and performance learning. In: Proceedings of IDEA 2013, pp. 27–35. ACM, New York (2013)Google Scholar
  5. 5.
    Bringmann, B., Nijssen, S., Tatti, N., Vreeken, J., Zimmermann, A.: Mining sets of patterns: next generation pattern mining. In: Tutorial at ICDM 2011 (2011)Google Scholar
  6. 6.
    De Bie, T.: An information theoretic framework for data mining. In: Proceedings of KDD 2011, pp. 564–572 (2011)Google Scholar
  7. 7.
    Dzyuba, V., van Leeuwen, M., Nijssen, S., Raedt, L.D.: Interactive learning of pattern rankings. International Journal on Artificial Intelligence Tools 23(6) (2014)Google Scholar
  8. 8.
    Goethals, B., Moens, S., Vreeken, J.: MIME: a framework for interactive visual pattern mining. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part III. LNCS, vol. 6913, pp. 634–637. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  9. 9.
    van Leeuwen, M., Knobbe, A.: Diverse subgroup set discovery. Data Mining and Knowledge Discovery 25, 208–242 (2012)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer ScienceKU LeuvenLeuvenBelgium

Personalised recommendations