Skip to main content

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8401))

  • 3713 Accesses

Abstract

We live in the era of data and need tools to discover valuable information in large amounts of data. The goal of exploratory data mining is to provide as much insight in given data as possible. Within this field, pattern set mining aims at revealing structure in the form of sets of patterns. Although pattern set mining has shown to be an effective solution to the infamous pattern explosion, important challenges remain.

One of the key challenges is to develop principled methods that allow user- and task-specific information to be taken into account, by directly involving the user in the discovery process. This way, the resulting patterns will be more relevant and interesting to the user. To achieve this, pattern mining algorithms will need to be combined with techniques from both visualisation and human-computer interaction. Another challenge is to establish techniques that perform well under constrained resources, as existing methods are usually computationally intensive. Consequently, they are only applied to relatively small datasets and on fast computers.

The ultimate goal is to make pattern mining practically more useful, by enabling the user to interactively explore the data and identify interesting structure. In this paper we describe the state-of-the-art, discuss open problems, and outline promising future directions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Agrawal, R., Imielinksi, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the SIGMOD 1993, pp. 207–216. ACM (1993)

    Google Scholar 

  2. 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 

  3. Guns, T., Nijssen, S., Raedt, L.D.: Itemset mining: A constraint programming perspective. Artif. Intell. 175(12-13), 1951–1983 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  4. Chau, D.H., Vreeken, J., van Leeuwen, M., Faloutsos, C. (eds.): Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics, IDEA 2013. ACM, New York (2013)

    Google Scholar 

  5. Atzmüller, M., Puppe, F.: Semi-automatic visual subgroup mining using vikamine. Journal of Universal Computer Science 11(11), 1752–1765 (2005)

    Google Scholar 

  6. Lucas, J.P., Jorge, A.M., Pereira, F., Pernas, A.M., Machado, A.A.: A tool for interactive subgroup discovery using distribution rules. In: Neves, J., Santos, M.F., Machado, J.M. (eds.) EPIA 2007. LNCS (LNAI), vol. 4874, pp. 426–436. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  7. 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 (LNAI), vol. 6913, pp. 634–637. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  8. Tuzhilin, A.: On subjective measures of interestingness in knowledge discovery. In: Proceedings of KDD 1995, pp. 275–281 (1995)

    Google Scholar 

  9. Kontonasios, K.N., Spyropoulou, E., De Bie, T.: Knowledge discovery interestingness measures based on unexpectedness. Wiley Int. Rev. Data Min. and Knowl. Disc. 2(5), 386–399 (2012)

    Article  Google Scholar 

  10. De Bie, T.: An information theoretic framework for data mining. In: Proceedings of KDD 2011, pp. 564–572 (2011)

    Google Scholar 

  11. Holzinger, A.: Human-computer interaction and knowledge discovery (hci-kdd): What is the benefit of bringing those two fields to work together? In: Cuzzocrea, A., Kittl, C., Simos, D.E., Weippl, E., Xu, L. (eds.) CD-ARES 2013. LNCS, vol. 8127, pp. 319–328. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  12. Keim, D.A., Andrienko, G., Fekete, J.-D., Görg, C., Kohlhammer, J., Melançon, G.: Visual analytics: Definition, process, and challenges. In: Kerren, A., Stasko, J.T., Fekete, J.-D., North, C. (eds.) Information Visualization. LNCS, vol. 4950, pp. 154–175. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  13. Klösgen, W.: Explora: A Multipattern and Multistrategy Discovery Assistant. In: Advances in Knowledge Discovery and Data Mining, pp. 249–271 (1996)

    Google Scholar 

  14. Wrobel, S.: An algorithm for multi-relational discovery of subgroups. In: Komorowski, J., Żytkow, J. (eds.) PKDD 1997. LNCS, vol. 1263, pp. 78–87. Springer, Heidelberg (1997)

    Chapter  Google Scholar 

  15. Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Discovering frequent closed itemsets for association rules. In: Beeri, C., Bruneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 398–416. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  16. Han, J., Cheng, H., Xin, D., Yan, X.: Frequent pattern mining: Current status and future directions. Data Mining and Knowledge Discovery 15(1), 55–86 (2007)

    Article  MathSciNet  Google Scholar 

  17. Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(8), 1226–1238 (2005)

    Article  Google Scholar 

  18. Vreeken, J., van Leeuwen, M., Siebes, A.: Krimp: mining itemsets that compress. Data Mining and Knowledge Discovery 23(1), 169–214 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  19. van Leeuwen, M., Vreeken, J., Siebes, A.: Compression picks item sets that matter. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 585–592. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  20. van Leeuwen, M., Vreeken, J., Siebes, A.: Identifying the components. Data Min. Knowl. Discov. 19(2), 173–292 (2009)

    Article  MathSciNet  Google Scholar 

  21. Vreeken, J., van Leeuwen, M., Siebes, A.: Characterising the difference. In: Proceedings of the KDD 2007, pp. 765–774 (2007)

    Google Scholar 

  22. Kralj Novak, P., Lavrač, N., Webb, G.: Supervised descriptive rule discovery: A unifying survey of contrast set, emerging pattern and subgroup mining. Journal of Machine Learning Research 10, 377–403 (2009)

    MATH  Google Scholar 

  23. Bhuiyan, M., Mukhopadhyay, S., Hasan, M.A.: Interactive pattern mining on hidden data: A sampling-based solution. In: Proceedings of CIKM 2012, pp. 95–104. ACM, New York (2012)

    Google Scholar 

  24. Dzyuba, V., van Leeuwen, M.: Interactive discovery of interesting subgroup sets. In: Tucker, A., Höppner, F., Siebes, A., Swift, S. (eds.) IDA 2013. LNCS, vol. 8207, pp. 150–161. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  25. van Leeuwen, M., Knobbe, A.: Diverse subgroup set discovery. Data Mining and Knowledge Discovery 25, 208–242 (2012)

    Article  MathSciNet  Google Scholar 

  26. Galbrun, E., Miettinen, P.: A Case of Visual and Interactive Data Analysis: Geospatial Redescription Mining. In: Instant Interactive Data Mining Workshop at ECML-PKDD 2012 (2012)

    Google Scholar 

  27. Boley, M., Mampaey, M., Kang, B., Tokmakov, P., Wrobel, S.: One Click Mining — Interactive Local Pattern Discovery through Implicit Preference and Performance Learning. In: Interactive Data Exploration and Analytics (IDEA) workshop at KDD 2013, pp. 28–36 (2013)

    Google Scholar 

  28. Dzyuba, V., van Leeuwen, M., Nijssen, S., Raedt, L.D.: Active preference learning for ranking patterns. In: Proceedings of ICTAI 2013, pp. 532–539 (2013)

    Google Scholar 

  29. Rüping, S.: Ranking interesting subgroups. In: Proceedings of ICML 2009, pp. 913–920 (2009)

    Google Scholar 

  30. Bie, T.D.: Maximum entropy models and subjective interestingness: an application to tiles in binary databases. Data Min. Knowl. Discov. 23(3), 407–446 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  31. Spyropoulou, E., Bie, T.D., Boley, M.: Interesting pattern mining in multi-relational data. Data Min. Knowl. Discov. 28(3), 808–849 (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

van Leeuwen, M. (2014). Interactive Data Exploration Using Pattern Mining. In: Holzinger, A., Jurisica, I. (eds) Interactive Knowledge Discovery and Data Mining in Biomedical Informatics. Lecture Notes in Computer Science, vol 8401. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43968-5_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-43968-5_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-43967-8

  • Online ISBN: 978-3-662-43968-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics