DART: a visual analytics system for understanding dynamic association rule mining


Dynamic rule mining can discover time-dependent association rules and provide more accurate descriptions about the relationship among items at different time periods and temporal granularities. However, users still face some challenges in analyzing and choosing reliable rules from the rules identified by algorithms, because of the large number of rules, the dynamic nature of rules across different time periods and granularities and the opacity of the relationship between rules and raw data. In this paper, we present our work on the development of DART, a visual analytics system for dynamic association rule mining, to help analysts gain a better understanding of rules and algorithms. DART allows users to explore rules at different time granularities (e.g., per hour, per day, per month, etc.) and with different time periods (e.g., daily, weekly, yearly, etc.), and to examine rules at multiple levels of detail, including investigating temporal patterns of a set of rules, comparing multiple rules, and evaluating a rule with raw data. Two case studies are used to show the functions and features of DART in analyzing business data and public safety data.

This is a preview of subscription content, access via your institution.

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


  1. 1.

    Keim, D., Andrienko, G., Fekete, J.-D.: Visual analytics: definition, process, and challenges. In: Kerren, A., Stasko, J.T., Fekete, J.-D., North, C. (eds.) Information Visualization, pp. 154–175. Springer, Berlin (2008)

    Google Scholar 

  2. 2.

    Thomas, J.J., Cook, K.A.: A visual analytics agenda. IEEE Comput. Graph. Appl. 26, 10–13 (2006)

    Article  Google Scholar 

  3. 3.

    Liu, S., Cui, W., Wu, Y., Liu, M.: A survey on information visualization: recent advances and challenges. Vis. Comput. 30, 1373–1393 (2014)

    Article  Google Scholar 

  4. 4.

    Keim, D.A.: Information visualization and visual data mining. IEEE Trans. Vis. Comput. Graph. 8, 1–8 (2002)

    Article  Google Scholar 

  5. 5.

    Ferreira de Oliveira, M.C., Levkowitz, H.: From visual data exploration to visual data mining: a survey. IEEE Trans. Vis. Comput. Graph. 9, 378–394 (2003)

    Article  Google Scholar 

  6. 6.

    Bertini, E., Lalanne, D.: Surveying the complementary role of automatic data analysis and visualization in knowledge discovery. In: Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration, VAKD ’09, (New York, NY, USA), pp. 12–20. ACM (2009)

  7. 7.

    Holzinger, A., Jurisica, I.: Interactive Knowledge Discovery and Data Mining in Biomedical Informatics: State-of-the-Art and Future Challenges, vol. 8401. Springer, New York (2014)

    Google Scholar 

  8. 8.

    Endert, A., Ribarsky, W., Turkay, C., Wong, B.W., Nabney, I., Blanco, I.D., Rossi, F.: The state of the art in integrating machine learning into visual analytics. Comput. Graph. Forum 36, 458–486 (2017)

    Article  Google Scholar 

  9. 9.

    Mühlbacher, T., Piringer, H., Gratzl, S., Sedlmair, M., Streit, M.: Opening the black box: strategies for increased user involvement in existing algorithm implementations. IEEE Trans. Vis. Comput. Graph. 20, 1643–1652 (2014)

    Article  Google Scholar 

  10. 10.

    Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. SIGMOD Rec. 22, 207–216 (1993)

    Article  Google Scholar 

  11. 11.

    Han, J., Kamber, M., Pei, J.: 6-mining frequent patterns, associations, and correlations: basic concepts and methods. In: Han, J., Kamber, M., Pei, J. (eds.) Data Mining. The Morgan Kaufmann Series in Data Management Systems, 3rd edn, pp. 243–278. Morgan Kaufmann, Boston (2012)

    Google Scholar 

  12. 12.

    Rong, G., Liu, J.-F., Gu, H.-J.: Mining dynamic association rules in databases. Control Theory Appl. 24(1), 127–131 (2007)

    Google Scholar 

  13. 13.

    Zhonglin, Z., Zongcheng, L., Chongyu, Q.: Tendency mining in dynamic association rules based on SVM classifier. Open Mech. Eng. J. 8, 303–307 (2014)

    Article  Google Scholar 

  14. 14.

    Uz Tansel, A., Imberman, S.P.: Discovery of association rules in temporal databases. In: Fourth International Conference on Information Technology (ITNG’07), pp. 371–376 (2007)

  15. 15.

    Liu, G., Suchitra, A., Zhang, H., Feng, M., Ng, S.-K., Wong, L.: AssocExplorer: an association rule visualization system for exploratory data analysis. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’12, (New York, NY, USA), pp. 1536–1539. ACM (2012)

  16. 16.

    Geng, L., Hamilton, H.J.: Interestingness measures for data mining: a survey. ACM Comput. Surv. 38, 9 (2006)

    Article  Google Scholar 

  17. 17.

    Weka 3: data mining software in java. https://www.cs.waikato.ac.nz/ml/weka/

  18. 18.

    Rapidminer. https://rapidminer.com/

  19. 19.

    Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min. Knowl. Discov. 8, 53–87 (2004)

    MathSciNet  Article  Google Scholar 

  20. 20.

    Ogihara, Z., Zaki, M., Parthasarathy, S., Ogihara, M., Li, W.: New algorithms for fast discovery of association rules. In: The 3rd International Conference on Knowledge Discovery and Data Mining (1997)

  21. 21.

    Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Discovering frequent closed itemsets for association rules. In: Proceedings of the 7th International Conference on Database Theory, ICDT ’99, (London, UK, UK), pp. 398–416. Springer (1999)

  22. 22.

    Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. SIGMOD Rec. 26, 255–264 (1997)

    Article  Google Scholar 

  23. 23.

    Djenouri, Y., Comuzzi, M.: Combining apriori heuristic and bio-inspired algorithms for solving the frequent itemsets mining problem. Inf. Sci. 420, 1–15 (2017)

    Article  Google Scholar 

  24. 24.

    Djenouri, Y., Chun-Wei Lin, J., Nørvåg, K., Ramampiaro, H.: Highly efficient pattern mining based on transaction decomposition. In: 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 1646–1649 (2019)

  25. 25.

    Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the Eleventh International Conference on Data Engineering, pp. 3–14 (1995)

  26. 26.

    Özden, B., Ramaswamy, S., Silberschatz, A.: Cyclic association rules. In: Proceedings of the Fourteenth International Conference on Data Engineering, ICDE ’98, (Washington, DC, USA), pp. 412–421. IEEE Computer Society (1998)

  27. 27.

    Nath, B., Bhattacharyya, D.K., Ghosh, A.: Incremental association rule mining: a survey. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 3, 157–169 (2013)

    Article  Google Scholar 

  28. 28.

    Bettini, C., Wang, X.S., Jajodia, S., Lin, J.: Discovering frequent event patterns with multiple granularities in time sequences. IEEE Trans. Knowl. Data Eng. 10, 222–237 (1998)

    Article  Google Scholar 

  29. 29.

    Bettini, C., Wang, X.S., Jajodia, S.: Testing complex temporal relationships involving multiple granularities and its application to data mining (extended abstract). In: Proceedings of the Fifteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, PODS ’96, (New York, NY, USA), pp. 68–78. ACM (1996)

  30. 30.

    Liu, Y., Salvendy, G.: Visualization to facilitate association rules modelling: a review. Ergon. IJE&HF 27(1), 11–23 (2005)

    Google Scholar 

  31. 31.

    Liu, Y., Salvendy, G.: Design and evaluation of visualization support to facilitate association rules modeling. Int. J. Hum. Comput. Interact. 21(1), 15–38 (2006)

    Article  Google Scholar 

  32. 32.

    Chen, W., Xie, C., Shang, P., Peng, Q.: Visual analysis of user-driven association rule mining. J. Vis. Lang. Comput. 42, 76–85 (2017)

    Article  Google Scholar 

  33. 33.

    Ong, K.-H., Ong, K.-L., Ng, W.-K., Lim, E.-P.: CrystalClear: active visualization of association rules. In: International Workshop on Active Mining (AM-2002) (2002)

  34. 34.

    Appice, A., Buono, P.: Analyzing multi-level spatial association rules through a graph-based visualization. In: International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pp. 448–458. Springer (2005)

  35. 35.

    Sekhavat, Y.A., Hoeber, O.: Visualizing association rules using linked matrix, graph, and detail views. Int. J. Intell. Sci. 3(01), 34 (2013)

    Article  Google Scholar 

  36. 36.

    Yang, L.: Visualizing frequent itemsets, association rules, and sequential patterns in parallel coordinates. In: Proceedings of the 2003 International Conference on Computational Science and Its Applications: Part I, ICCSA’03, (Berlin, Heidelberg), pp. 21–30. Springer (2003)

  37. 37.

    Yang, L.: Pruning and visualizing generalized association rules in parallel coordinates. IEEE Trans. Knowl. Data Eng. 17, 60–70 (2005)

    Article  Google Scholar 

  38. 38.

    Ankerst, M.: Visual data mining with pixel-oriented visualization techniques. In: Proceedings of the ACM SIGKDD Workshop on Visual Data Mining (2001)

  39. 39.

    Chakravarthy, S., Zhang, H.: Visualization of association rules over relational DBMSs. In: Proceedings of the 2003 ACM Symposium on Applied Computing, SAC ’03, (New York, NY, USA), pp. 922–926. ACM (2003)

  40. 40.

    Wong, P.C., Whitney, P., Thomas, J.: Visualizing association rules for text mining. In: Proceedings 1999 IEEE Symposium on Information Visualization (InfoVis’99), pp. 120–123 (1999)

  41. 41.

    Bruzzese, D., Davino, C.: Visual post-analysis of association rules. J. Vis. Lang. Comput. 14(6), 621–635 (2003)

    Article  Google Scholar 

  42. 42.

    Galiano, F.B., Blanco, I.J., Sánchez, D., Vila, M.A.: Measuring the accuracy and interest of association rules: a new framework. Intell. Data Anal. 6, 221–235 (2002)

    Article  Google Scholar 

  43. 43.

    Shortliffe, E.H., Buchanan, B.G.: A model of inexact reasoning in medicine. Math. Biosci. 23(3), 351–379 (1975)

    MathSciNet  Article  Google Scholar 

  44. 44.

    Liu, B., Hsu, W., Wang, K., Chen, S.: Visually aided exploration of interesting association rules. In: Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining, PAKDD ’99, (London, UK, UK), pp. 380–389. Springer (1999)

  45. 45.

    Delgado, M., Ruiz, M.D., Sánchez, D.: Studying interest measures for association rules through a logical model. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 18, 87–106 (2010)

    MathSciNet  Article  Google Scholar 

  46. 46.

    Zhao, X., Wu, Y., Lee, D.L., Cui, W.: iforest: Interpreting random forests via visual analytics. IEEE Trans. Vis. Comput. Graph. 25, 407–416 (2019)

    Article  Google Scholar 

  47. 47.

    El-Assady, M., Sperrle, F., Deussen, O., Keim, D., Collins, C.: Visual analytics for topic model optimization based on user-steerable speculative execution. IEEE Trans. Vis. Comput. Graph. 25, 374–384 (2019)

    Article  Google Scholar 

  48. 48.

    Zhao, H., Zhang, H., Liu, Y., Zhang, Y., Zhang, X.L.: Pattern discovery: a progressive visual analytic design to support categorical data analysis. J. Vis. Lang. Comput. 43, 42–49 (2017)

    Article  Google Scholar 

  49. 49.

    Tableau desktop. https://www.tableau.com/products/

  50. 50.

    Guo, P., Xiao, H., Wang, Z., Yuan, X.: Interactive local clustering operations for high dimensional data in parallel coordinates. In: 2010 IEEE Pacific Visualization Symposium (PacificVis), pp. 97–104 (2010)

  51. 51.

    Sedlmair, M., Munzner, T., Tory, M.: Empirical guidance on scatterplot and dimension reduction technique choices. IEEE Trans. Vis. Comput. Graph. 19, 2634–2643 (2013)

    Article  Google Scholar 

  52. 52.

    Hoffman, P., Grinstein, G., Marx, K., Grosse, I., Stanley, E.: DNA visual and analytic data mining. In: Proceedings. Visualization ’97 (Cat. No. 97CB36155), pp. 437–441 (1997)

  53. 53.

    Wang, Y.C., Zhang, Q., Lin, F., Goh, C.K., Seah, H.S.: Polarviz: a discriminating visualization and visual analytics tool for high-dimensional data. Vis. Comput. 35, 1567–1582 (2019)

    Article  Google Scholar 

  54. 54.

    Online retail dataset. http://archive.ics.uci.edu/ml/datasets/

  55. 55.

    Nhtsa fatality analysis reporting system. http://www.nhtsa.gov/FARS

  56. 56.

    Kosters, W.A., Pijls, W., Popova, V.: Complexity analysis of depth first and fp-growth implementations of apriori. In: Proceedings of the 3rd International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM’03, (Berlin, Heidelberg), pp. 284–292. Springer (2003)

Download references


This work was supported in part by the National Natural Science Foundation of China (No. 61572344).

Author information



Corresponding author

Correspondence to Xiaolong Zhang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (mp4 28495 KB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, H., Chen, J., Qiang, Y. et al. DART: a visual analytics system for understanding dynamic association rule mining. Vis Comput 37, 341–357 (2021). https://doi.org/10.1007/s00371-020-01803-x

Download citation


  • Visual analytics
  • Dynamic association rule
  • Sensemaking
  • Data mining