Decision Trees in Data Stream Mining

  • Leszek RutkowskiEmail author
  • Maciej Jaworski
  • Piotr Duda
Part of the Studies in Big Data book series (SBD, volume 56)


A decision tree [1] is a data mining tool commonly used in data classification tasks. Apart from providing satisfactorily high accuracies, the results produced by decision trees are easily interpretable. A decision tree, in fact, divides attribute values space X into disjoint subspaces. The most common decision tree induction algorithms for static data sets are the ID3 algorithm [2], the C4.5 algorithm [3, 4], and the CART algorithm [5].


  1. 1.
    Pinder, J.P.: Decision trees. In: Pinder, J.P. (ed.) Introduction to Business Analytics using Simulation, pp. 47–69. Academic Press, Boston (2017)Google Scholar
  2. 2.
    Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)Google Scholar
  3. 3.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)Google Scholar
  4. 4.
    Yang, Y., Chen, W.: Taiga: performance optimization of the C4.5 decision tree construction algorithm. Tsinghua Sci. Technol. 21, 415–425 (2016)CrossRefGoogle Scholar
  5. 5.
    Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Wadsworth and Brooks, Monterey (1984)zbMATHGoogle Scholar
  6. 6.
    Lomax, S., Vadera, S.: A cost-sensitive decision tree learning algorithm based on a multi-armed bandit framework. Comput. J. 60, 941–956 (2017)MathSciNetGoogle Scholar
  7. 7.
    Li, J., Ma, S., Le, T., Liu, L., Liu, J.: Causal decision trees. IEEE Trans. Knowl. Data Eng. 29, 257–271 (2017)CrossRefGoogle Scholar
  8. 8.
    Pei, S., Hu, Q.: Partially monotonic decision trees. Inf. Sci. 424, 104–117 (2018)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Wang, L., Li, Q., Yu, Y., Liu, J.: Region compatibility based stability assessment for decision trees. Expert. Syst. Appl. 105, 112–128 (2018)CrossRefGoogle Scholar
  10. 10.
    Nguyen, K., Tran, D., Ma, W., Sharma, D.: Decision tree algorithms for image data type identification. Log. J. IGPL 25, 67–82 (2017)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Segatori, A., Marcelloni, F., Pedrycz, W.: On distributed fuzzy decision trees for big data. IEEE Trans. Fuzzy Syst. 26, 174–192 (2018)CrossRefGoogle Scholar
  12. 12.
    Domingos, P., Hulten, G.: Mining high-speed data streams. In: Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 71–80 (2000)Google Scholar
  13. 13.
    Hoeffding, W.: Probability inequalities for sums of bounded random variables. J. Am. Stat. Assoc. 58, 13–30 (1963)MathSciNetCrossRefGoogle Scholar
  14. 14.
    From, S.G., Swift, A.W.: A refinement of Hoeffding’s inequality. J. Stat. Comput. Simul. 83(5), 977–983 (2013)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Rutkowski, L., Pietruczuk, L., Duda, P., Jaworski, M.: Decision trees for mining data streams based on the McDiarmid’s bound. IEEE Trans. Knowl. Data Eng. 25(6), 1272–1279 (2013)CrossRefGoogle Scholar
  16. 16.
    Rutkowski, L., Jaworski, M., Pietruczuk, L., Duda, P.: A new method for data stream mining based on the misclassification error. IEEE Trans. Neural Netw. Learn. Syst. 26(5), 1048–1059 (2015)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Gama, J.: Accurate decision trees for mining high-speed data streams. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 523–528. ACM Press (2003)Google Scholar
  18. 18.
    Kirkby, R.: Improving Hoeffding trees. Ph.D. thesis, University of Waikato (2007)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Leszek Rutkowski
    • 1
    • 2
    Email author
  • Maciej Jaworski
    • 1
  • Piotr Duda
    • 1
  1. 1.Institute of Computational IntelligenceCzestochowa University of TechnologyCzęstochowaPoland
  2. 2.Information Technology InstituteUniversity of Social SciencesLodzPoland

Personalised recommendations