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

Abstract

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.

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References

  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)

  2. 2.

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

  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)

  4. 4.

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

  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)

  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)

  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)

  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)

  10. 10.

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

  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)

  12. 12.

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

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  43. 43.

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

  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)

  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)

  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)

  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)

  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)

  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)

  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)

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Funding

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

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Correspondence to Xiaolong Zhang.

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Zhang, H., Chen, J., Qiang, Y. et al. DART: a visual analytics system for understanding dynamic association rule mining. Vis Comput (2020). https://doi.org/10.1007/s00371-020-01803-x

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Keywords

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