Tiler: Software for Human-Guided Data Exploration

  • Andreas HeneliusEmail author
  • Emilia Oikarinen
  • Kai Puolamäki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11053)


Understanding relations in datasets is important for the successful application of data mining and machine learning methods. This paper describes tiler, a software tool for interactive visual explorative data analysis realising the interactive Human-Guided Data Exploration framework. tiler allows a user to formulate different hypotheses concerning the relations in a dataset. Data samples corresponding to these hypotheses are then compared visually, allowing the user to gain insight into relations in the dataset. The exploration process is iterative and the user gradually builds up his or her understanding of the data. Code related to this paper is available at:



This work has been supported by the Academy of Finland (decisions 319145 and 313513).


  1. 1.
    De Bie, T.: Subjective interestingness in exploratory data mining. In: Tucker, A., Höppner, F., Siebes, A., Swift, S. (eds.) IDA 2013. LNCS, vol. 8207, pp. 19–31. Springer, Heidelberg (2013). Scholar
  2. 2.
    De Bie, T.: An information theoretic framework for data mining. In: KDD, pp. 564–572 (2011)Google Scholar
  3. 3.
    De Bie, T.: Maximum entropy models and subjective interestingness: an application to tiles in binary databases. Data Min. Knowl. Discov. 23(3), 407–446 (2011)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Dheeru, D., Karra Taniskidou, E.: UCI machine learning repository (2017).
  5. 5.
    Hanhijärvi, S., Ojala, M., Vuokko, N., Puolamäki, K., Tatti, N., Mannila, H.: Tell me something I don’t know: randomization strategies for iterative data mining. In: KDD, pp. 379–388 (2009)Google Scholar
  6. 6.
    Henelius, A., Oikarinen, E., Puolamäki, K.: Human-guided data exploration. arXiv preprint, arXiv:1804.03194 (2018)
  7. 7.
    Kang, B., Puolamäki, K., Lijffijt, J., De Bie, T.: A tool for subjective and interactive visual data exploration. In: Berendt, B., et al. (eds.) ECML PKDD 2016. LNCS (LNAI), vol. 9853, pp. 3–7. Springer, Cham (2016). Scholar
  8. 8.
    Puolamäki, K., Kang, B., Lijffijt, J., De Bie, T.: Interactive visual data exploration with subjective feedback. In: Frasconi, P., Landwehr, N., Manco, G., Vreeken, J. (eds.) ECML PKDD 2016. LNCS (LNAI), vol. 9852, pp. 214–229. Springer, Cham (2016). Scholar
  9. 9.
    Puolamäki, K., Oikarinen, E., Kang, B., Lijffijt, J., De Bie, T.: Interactive visual data exploration with subjective feedback: an information-theoretic approach. In: ICDE, pp. 1208–1211 (2018)Google Scholar
  10. 10.
    Tukey, J.W.: Exploratory Data Analysis. Addison-Wesley, Boston (1977)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Andreas Henelius
    • 1
    • 2
    Email author
  • Emilia Oikarinen
    • 1
    • 2
  • Kai Puolamäki
    • 1
    • 2
  1. 1.Department of Computer ScienceAalto UniversityHelsinkiFinland
  2. 2.Department of Computer ScienceUniversity of HelsinkiHelsinkiFinland

Personalised recommendations