Redescription mining augmented with random forest of multi-target predictive clustering trees


In this work, we present a redescription mining algorithm that uses Random Forest of Predictive Clustering Trees (RFPCTs) for generating and iteratively improving a set of redescriptions. The approach uses information about element membership in different queries, generated from a single constructed PCT, to explore redescription space, while queries obtained from the Random Forest of PCTs increase candidate diversity. The approach is able to produce highly accurate, statistically significant redescriptions described by Boolean, nominal or numerical attributes. As opposed to current tree-based approaches that use multi-class or binary classification, we explore the benefits of using multi-label classification and multi-target regression to create redescriptions. Major benefit of the approach, compared to other state of the art solutions, is that it does not require specifying minimal threshold on redescription accuracy to obtain highly accurate, optimized set of redescriptions. The process of Random Forest based augmentation and different modes of redescription set creation are evaluated on three datasets with different properties. We use the same datasets to compare the performance of our algorithm to state of the art redescription mining approaches.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10


  1. Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD international conference on management of data (pp. 207–216). Washington: D.C.

  2. Bickel, S., & Scheffer, T. (2004). Multi-View Clustering. In Proceedings of the 4th IEEE international conference on data mining, 19–26, Washington.

  3. Blockeel, H. (1998). Top-down induction of first order logical decision trees. Phd thesis, Katholieke Universiteit Leuven, Department of Computer Science.

    Google Scholar 

  4. Bringmann, B., & Zimmermann, A. (2007). The chosen few: on identifying valuable patterns. In Proceedings of the 7th IEEE international conference on data mining (pp. 63–72). Omaha.

  5. Cohen, E., Datar, M., Fujiwara, S., Gionis, A., Indyk, P., Motwani, R., Ullman, J.D., & Yang, C. (2000). Finding interesting associations without support pruning. In ICDE, 489–499.

  6. DBLP dataset (2010).

  7. Galbrun, E. (2013). Methods for Redescription mining. Phd thesis, University of Helsinki.

  8. Galbrun, E., & Kimmig, A. (2014). Finding relational redescriptions. Machine Learning, 225–248.

  9. Galbrun, E., & Miettinen, P. (2012a). From black and white to full color: extending redescription mining outside the Boolean world. Statistical Analysis and Data Mining, 284–303.

  10. Galbrun, E., & Miettinen, P. (2012b). Siren an interactive tool for mining and visualizing geospatial redescriptions. KDD, 1544–1547.

  11. Galbrun, E., & Miettinen, P. (2012c). A Case of Visual and Interactive Data Analysis: Geospatial Redescription Mining. Instant Interactive Data Mining Workshop @ ECML-PKDD.

  12. Gallo, A., Miettinen, P., & Mannila, H. (2008). Finding subgroups having several descriptions: algorithms for redescription mining. In Proceedings of the SIAM international conference on data mining (pp. 334–345). Georgia: Atlanta.

  13. Gamberger, D., & Lavrač, N. (2002). Expert-guided subgroup discovery: methodology and application. Journal of Artificial Intelligence Research, 17, 501–527.

    MATH  Google Scholar 

  14. Gamberger, D., Mihelčić, M., & Lavrač, N. (2014). Multilayer clustering, a discovery experiment on country level trading data. In Proceedings of the 17th international conference on discovery science (pp. 87–98). Slovenia: Bled.

  15. Giacometti, A., Li, D.H., Marcel, P., & Soulet, A. (2014). 20 Years of pattern mining: a bibliometric survey. SIGKDD Explor. Newsl., 41–50.

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

    MathSciNet  Article  Google Scholar 

  17. Hijmans, R.J., Cameron, S., Parra, L., Jones, P., & Jarvis, A. (2005). Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25, 1965–978.

    Article  Google Scholar 

  18. Knobbe, A.J., & Ho, E.K.Y. (2006). Pattern teams. In Proceedings of the 10th european conference on principles and practice of knowledge discovery in databases (pp. 577–584). Germany: Berlin.

  19. Kocev, D.K., Vens, C., Struyf, J., & Džeroski, S. (2013). Tree ensembles for predicting structured outputs. Pattern Recognition, 817–833.

  20. Lavrač, N., Kavšek, B., Flach, P., & Todorovski, Lj. (2004). Subgroup discovery with CN2-SD. Journal of Machine Learning Research, 5, 153–188.

    MathSciNet  Google Scholar 

  21. Mihelčić, M., Džeroski, S., Lavrač, N., & Šmuc, T. (2015a). Redescription mining with multi-label predictive clustering trees. In Proceedings of the 4th workshop on new frontiers in mining complex patterns (pp. 86–97). Portugal: Porto.

  22. Mihelčić, M., Džeroski, S., Lavrač, N., & Šmuc, T. (2015b). Redescription mining with multi-target predictive clustering trees (2015b). In New frontiers in mining complex patterns - 4th international workshop, NFMCP 2015, held in conjunction with ECML-PKDD 2015, porto, Portugal, September 7, 2015, Revised Selected Papers, (Vol. 9607 pp. 125–143).

  23. Mitchell-Jones, A.J., Amori, G., Bogdanowicz, W., Krystufe, B., Reijnders, P., Spitzenberger, F., Stubbe, M., Thissen, J., Vohralik, V., & Zima, J. (1999). The atlas of european mammals. London: Academic Press.

    Google Scholar 

  24. Mooney, C.H., & Roddick, J.F (2013). Sequential pattern mining – approaches and algorithms. ACM Computing Surveys, 45(2).

  25. Parida, L., & Ramakrishnan, N. (2004). Redescription mining: structure theory and algorithms. In Proceedings of the 20th national conference on artificial intelligence (pp. 837–844). Pennsylvania: Pittsburgh.

  26. Piccart, B. (2012). Algorithms for multi-target learning. Phd thesis, Katholieke Universiteit Leuven.

  27. Ramakrishnan, N., Kumar, D., Mishra, B., Potts, M., & Helm, R.F. (2004). Turning CARTwheels: an alternating algorithm for mining redescriptions. In Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining (pp. 266–275). Seattle, WA: ACM.

  28. Stojanova, D., Ceci, M., Appice, A., & Džeroski, S. (2012). Network regression with predictive clustering trees. Data Mining and Knowledge Discovery, 378–413.

  29. UNCTAD Database,

  30. van Leeuwen, M., & Galbrun, E. (2015). Association discovery in two-view data. IEEE Transactions on Knowledge and Data Engineering, 27, 3190–3202.

  31. World bank database,

  32. Zaki, M.J., & Ramakrishnan, N. (2005). Reasoning about sets using redescription mining. In Proceedings of the 11th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 364–373). Chicago, Illinois: ACM.

  33. Zinchenko, T. (2014). Redescription mining over non-binary data sets using decision trees. Masters thesis, Universität des Saarlandes.

Download references


The authors would like to acknowledge the European Commission’s support through the MAESTRA project (Gr. no. 612944), the MULTIPLEX project ( 317532), the InnoMol project (Gr. no. 316289), and support of the Croatian Science Foundation (Pr. no. 9623: Machine Learning Algorithms for Insightful Analysis of Complex Data Structures).

Author information



Corresponding author

Correspondence to Matej Mihelčić.

Electronic supplementary material

Below is the link to the electronic supplementary material.

(PDF 284 KB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Mihelčić, M., Džeroski, S., Lavrač, N. et al. Redescription mining augmented with random forest of multi-target predictive clustering trees. J Intell Inf Syst 50, 63–96 (2018).

Download citation


  • Knowledge discovery
  • Redescription mining
  • Random forest
  • Predictive clustering trees
  • World countries
  • Computer science bibliography
  • Bioclimatic niches