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Visualization and Visual Analytic Techniques for Patterns

  • Wolfgang JentnerEmail author
  • Daniel A. Keim
Chapter
Part of the Studies in Big Data book series (SBD, volume 51)

Abstract

This chapter surveys visualization techniques for frequent itemsets, association rules, and sequential patterns. The human is crucial in the process of identifying interesting patterns and thus, mining such patterns and visualizing them is important for the decision making. The complementary feedback loop that a user may use to refine parameters through inspecting the current mining results is broadly described as visual analytics. This survey identifies visual designs for patterns of each category and analyzes and compares their strengths and weaknesses systematically. The comparison and overview help decision-makers selecting the appropriate technique for their tasks and systems while knowing about their limitations.

References

  1. 1.
    Aggarwal, C.C., Han, J. (eds.): Frequent Pattern Mining. Springer, Berlin (2014)zbMATHGoogle Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) VLDB’94, Proceedings of 20th International Conference on Very Large Data Bases, 12–15 September 1994, Santiago de Chile, Chile, pp. 487–499. Morgan Kaufmann (1994)Google Scholar
  3. 3.
    Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the Eleventh International Conference on Data Engineering, 1995, pp. 3–14. IEEE (1995)Google Scholar
  4. 4.
    Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. SIGMOD Rec. 22(2), 207–216 (1993)CrossRefGoogle Scholar
  5. 5.
    Agrawal, R., Mehta, M., Shafer, J.C., Srikant, R., Arning, A., Bollinger, T.: The quest data mining system. In: KDD, pp. 244–249. AAAI Press (1996)Google Scholar
  6. 6.
    Aigner, W., Miksch, S., Müller, W., Schumann, H., Tominski, C.: Visualizing time-oriented data - a systematic view. Comput. Graph. 31(3), 401–409 (2007)CrossRefGoogle Scholar
  7. 7.
    Aigner, W., Miksch, S., Schumann, H., Tominski, C.: Visualization of Time-Oriented Data. Human-Computer Interaction Series. Springer, Berlin (2011)CrossRefGoogle Scholar
  8. 8.
    Alsallakh, B., Micallef, L., Aigner, W., Hauser, H., Miksch, S., Rodgers, P.J.: The state-of-the-art of set visualization. Comput. Graph. Forum 35(1), 234–260 (2016)CrossRefGoogle Scholar
  9. 9.
    Andrienko, G.L., Andrienko, N.V., Bak, P., Keim, D.A., Wrobel, S.: Visual Analytics of Movement. Springer, Berlin (2013)CrossRefGoogle Scholar
  10. 10.
    Behrisch, M., Streeb, D., Stoffel, F., Seebacher, D., Matejek, B., Weber, S.H., Mittelstädt, S., Pfister, H., Keim, D.: Commercial visual analytics systems - advances in the big data analytics field. IEEE Trans. Vis. Comput. Graph. (2018). To appearGoogle Scholar
  11. 11.
    Bernard, J., Wilhelm, N., Krüger, B., May, T., Schreck, T., Kohlhammer, J.: Motionexplorer: exploratory search in human motion capture data based on hierarchical aggregation. IEEE Trans. Vis. Comput. Graph. 19(12), 2257–2266 (2013)CrossRefGoogle Scholar
  12. 12.
    Bernard, J., Sessler, D., May, T., Schlomm, T., Pehrke, D., Kohlhammer, J.: A visual-interactive system for prostate cancer cohort analysis. IEEE Comput. Graph. Appl. 35(3), 44–55 (2015)CrossRefGoogle Scholar
  13. 13.
    Bertin, J.: Sémiologie graphique: Les diagrammes-les réseaux-les cartes (1973)Google Scholar
  14. 14.
    Bertin, J.: Semiology of graphics: diagrams, networks, maps (1983)Google Scholar
  15. 15.
    Bodesinsky, P., Alsallakh, B., Gschwandtner, T., Miksch, S.: Exploration and assessment of event data. In: Proceedings of EuroVis Workshop on Visual Analytics (2015)Google Scholar
  16. 16.
    Borgelt, C.: Frequent item set mining. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2(6), 437–456 (2012)CrossRefGoogle Scholar
  17. 17.
    Bothorel, G., Serrurier, M., Hurter, C.: Visualization of frequent itemsets with nested circular layout and bundling algorithm. In: ISVC (2). Lecture Notes in Computer Science, vol. 8034, pp. 396–405. Springer, Berlin (2013)CrossRefGoogle Scholar
  18. 18.
    Brunk, C., Kelly, J., Kohavi, R.: MineSet: an integrated system for data mining. In: KDD, pp. 135–138 (1997)Google Scholar
  19. 19.
    Burdick, D., Calimlim, M., Gehrke, J.: MAFIA: a maximal frequent itemset algorithm for transactional databases. In: ICDE, pp. 443–452. IEEE Computer Society (2001)Google Scholar
  20. 20.
    Cappers, B.C.M., van Wijk, J.J.: Exploring multivariate event sequences using rules, aggregations, and selections. IEEE Trans. Vis. Comput. Graph. 24(1), 532–541 (2018)CrossRefGoogle Scholar
  21. 21.
    Chen, Y., Xu, P., Ren, L.: Sequence synopsis: optimize visual summary of temporal event data. IEEE Trans. Vis. Comput. Graph. 24(1), 45–55 (2018)CrossRefGoogle Scholar
  22. 22.
    Chou, J., Wang, Y., Ma, K.: Privacy preserving event sequence data visualization using a Sankey diagram-like representation. In: SIGGRAPH Asia Symposium on Visualization, pp. 1:1–1:8. ACM (2016)Google Scholar
  23. 23.
    Collier, G.H.: Thoth-II: hypertext with explicit semantics. In: Smith, J.B., Halasz, F.G. (eds.) Hypertext’87 Proceedings, 13–15 November 1987, Chapel Hill, North Carolina, USA, pp. 269–289. ACM (1987)Google Scholar
  24. 24.
    Ellis, G., Mansmann, F.: Mastering the information age solving problems with visual analytics. In: Eurographics, vol. 2, p. 5 (2010)Google Scholar
  25. 25.
    Ellis, G.P., Dix, A.J.: The plot, the clutter, the sampling and its lens: occlusion measures for automatic clutter reduction. In: AVI, pp. 266–269. ACM Press (2006)Google Scholar
  26. 26.
    Fournier-Viger, P., Gomariz, A., Campos, M., Thomas, R.: Fast vertical mining of sequential patterns using co-occurrence information. In: PAKDD (1). Lecture Notes in Computer Science, vol. 8443, pp. 40–52. Springer, Berlin (2014)CrossRefGoogle Scholar
  27. 27.
    Fournier-Viger, P., Lin, J.C., Gomariz, A., Gueniche, T., Soltani, A., Deng, Z., Lam, H.T.: The SPMF open-source data mining library version 2. In: ECML/PKDD (3). Lecture Notes in Computer Science, vol. 9853, pp. 36–40. Springer, Berlin (2016)CrossRefGoogle Scholar
  28. 28.
    Fournier-Viger, P., Lin, J.C., Vo, B., Truong, T.C., Zhang, J., Le, H.B.: A survey of itemset mining. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 7(4) (2017)Google Scholar
  29. 29.
    Fournier-Viger, P., Lin, J.C.-W., Kiran, R.U., Koh, Y.S., Thomas, R.: A survey of sequential pattern mining. Data Sci. Pattern Recognit. 1(1), 54–77 (2017)Google Scholar
  30. 30.
    Fukuda, T., Morimoto, Y., Morishita, S., Tokuyama, T.: Data mining using two-dimensional optimized association rules: scheme, algorithms, and visualization. ACM SIGMOD Rec. 25(2), 13–23 (1996)CrossRefGoogle Scholar
  31. 31.
    Geng, L., Hamilton, H.J.: Interestingness measures for data mining: a survey. ACM Comput. Surv. 38(3), 9 (2006)CrossRefGoogle Scholar
  32. 32.
    Giannotti, F., Nanni, M., Pinelli, F., Pedreschi, D.: Trajectory pattern mining. In: KDD, pp. 330–339. ACM (2007)Google Scholar
  33. 33.
    Goethals, B.: Survey on frequent pattern mining. Univ. Helsinki 19, 840–852 (2003)Google Scholar
  34. 34.
    Goethals, B., Zaki, M.J. (eds.): FIMI ’03, Frequent Itemset Mining Implementations, Proceedings of the ICDM 2003 Workshop on Frequent Itemset Mining Implementations, 19 December 2003, Melbourne, Florida, USA. CEUR Workshop Proceedings, vol. 90 (2003). www.CEUR-WS.org
  35. 35.
    Goethals, Jr., R.J.B., Zaki, M.J. (eds.): FIMI ’04, Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations, Brighton, UK, 1 November 2004. CEUR Workshop Proceedings, vol. 126 (2005). www.CEUR-WS.org
  36. 36.
    Gotz, D., Stavropoulos, H.: DecisionFlow: visual analytics for high-dimensional temporal event sequence data. IEEE Trans. Vis. Comput. Graph. 20(12), 1783–1792 (2014)CrossRefGoogle Scholar
  37. 37.
    Gotz, D., Wang, F., Perer, A.: A methodology for interactive mining and visual analysis of clinical event patterns using electronic health record data. J. Biomed. Inform. 48, 148–159 (2014)CrossRefGoogle Scholar
  38. 38.
    Grünwald, P.: A tutorial introduction to the minimum description length principle. CoRR arXiv:math.ST/0406077 (2004)
  39. 39.
    Guzdial, M., Walton, C., Konemann, M., Soloway, E.: Characterizing process change using log file data. Technical report, Georgia Institute of Technology (1993)Google Scholar
  40. 40.
    Hahsler, M., Chelluboina, S.: Visualizing association rules: introduction to the r-extension package arulesViz. R Project Module, pp. 223–238 (2011)Google Scholar
  41. 41.
    Han, J: Mining knowledge at multiple concept levels. In: CIKM, pp. 19–24. ACM (1995)Google Scholar
  42. 42.
    Han, J., Cercone, N.: Aviz: a visualization system for discovering numeric association rules. In: PAKDD. Lecture Notes in Computer Science, vol. 1805, pp. 269–280. Springer, Berlin (2000)CrossRefGoogle Scholar
  43. 43.
    Han, J., Fu, Y., Wang, W., Chiang, J., Gong, W., Koperski, K., Li, D., Lu, Y., Rajan, A., Stefanovic, N., Xia, B., Zaïane, O.R.: DBMiner: a system for mining knowledge in large relational databases. In: KDD, pp. 250–255. AAAI Press (1996)Google Scholar
  44. 44.
    Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Chen, W., Naughton, J.F., Bernstein, P.A. (eds.) Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 16–18 May 2000, Dallas, Texas, USA, pp. 1–12. ACM (2000)Google Scholar
  45. 45.
    Hartigan, J.A., Kleiner, B.: Mosaics for contingency tables. In: Computer Science and Statistics: Proceedings of the 13th Symposium on the Interface, pp. 268–273. Springer, Berlin (1981)CrossRefGoogle Scholar
  46. 46.
    Hipp, J., Güntzer, U., Nakhaeizadeh, G.: Algorithms for association rule mining a general survey and comparison. ACM SIGKDD Explor. Newsl. 2(1), 58–64 (2000)CrossRefGoogle Scholar
  47. 47.
    Hoaglin, D., Mosteller, F., Tukey, J.: Understanding robust and exploratory data analysis (1983)Google Scholar
  48. 48.
    Hofmann, H.: Exploring categorical data: interactive mosaic plots. Metrika 51(1), 11–26 (2000)zbMATHCrossRefGoogle Scholar
  49. 49.
    Hofmann, H., Siebes, A., Wilhelm, A.F.X: Visualizing association rules with interactive mosaic plots. In: Ramakrishnan, R., Stolfo, S.J., Bayardo, R.J., Parsa, I. (eds.) Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, MA, USA, 20–23 August 2000, pp. 227–235. ACM (2000)Google Scholar
  50. 50.
    Hu, M., Wongsuphasawat, K., Stasko, J.T.: Visualizing social media content with sententree. IEEE Trans. Vis. Comput. Graph. 23(1), 621–630 (2017)CrossRefGoogle Scholar
  51. 51.
    Jentner, W., El-Assady, M., Gipp, B., Keim, D.A.: Feature alignment for the analysis of verbatim text transcripts. In: EuroVA 2017: EuroVis Workshop on Visual Analytics, pp. 13–18 (2017)Google Scholar
  52. 52.
    Jentner, W., Sacha, D., Stoffel, F., Ellis, G., Zhang, L., Keim, D.A.: Making machine intelligence less scary for criminal analysts: reflections on designing a visual comparative case analysis tool. Vis. Comput. J. (2018)Google Scholar
  53. 53.
    Keim, D.A.: Information visualization and visual data mining. IEEE Trans. Vis. Comput. Graph. 8(1), 1–8 (2002)MathSciNetCrossRefGoogle Scholar
  54. 54.
    Keim, D.A., Schneidewind, J., Sips, M.: FP-Viz: visual frequent pattern mining. In: InfoVis (2005)Google Scholar
  55. 55.
    Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H., Verkamo, A.I.: Finding interesting rules from large sets of discovered association rules. In: CIKM, pp. 401–407. ACM (1994)Google Scholar
  56. 56.
    Kotsiantis, S., Kanellopoulos, D.: Association rules mining: a recent overview. GESTS Int. Trans. Comput. Sci. Eng. 32(1), 71–82 (2006)Google Scholar
  57. 57.
    Kruskal, J.B., Landwehr, J.M.: Icicle plots: better displays for hierarchical clustering. Am. Stat. 37(2), 162–168 (1983)Google Scholar
  58. 58.
    Lam, H., Russell, D.M., Tang, D., Munzner, T.: Session viewer: visual exploratory analysis of web session logs. In: Proceedings of the IEEE Symposium on Visual Analytics Science and Technology, IEEE VAST 2007, Sacramento, California, USA, 30 October–1 November 2007, pp. 147–154. IEEE Computer Society (2007)Google Scholar
  59. 59.
    Lee, I., Cai, G., Lee, K.: Mining points-of-interest association rules from geo-tagged photos. In: 46th Hawaii International Conference on System Sciences, HICSS 2013, Wailea, HI, USA, 7–10 January 2013, pp. 1580–1588. IEEE Computer Society (2013)Google Scholar
  60. 60.
    Leung, C.K., Carmichael, C.L.: FpVAT: a visual analytic tool for supporting frequent pattern mining. SIGKDD Explor. 11(2), 39–48 (2009)CrossRefGoogle Scholar
  61. 61.
    Leung, C.K., Irani, P., Carmichael, C.L.: FIsViz: a frequent itemset visualizer. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) Advances in Knowledge Discovery and Data Mining, 12th Pacific-Asia Conference, PAKDD 2008, Osaka, Japan, 20–23 May 2008 Proceedings. Lecture Notes in Computer Science, vol. 5012, pp. 644–652. Springer, Berlin (2008)Google Scholar
  62. 62.
    Leung, C.K., Irani, P., Carmichael, C.L.: WiFIsViz: effective visualization of frequent itemsets. In: Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), 15–19 December 2008, Pisa, Italy, pp. 875–880. IEEE Computer Society (2008)Google Scholar
  63. 63.
    Leung, C.K., Jiang, F., Irani, P.P.: FpMapViz: a space-filling visualization for frequent patterns. In: Spiliopoulou, M., Wang, H., Cook, D.J., Pei, J., Wang, W., Zaïane, O.R., Wu, X. (eds.) 2011 IEEE 11th International Conference on Data Mining Workshops (ICDMW), Vancouver, BC, Canada, 11 December 2011, pp. 804–811. IEEE Computer Society (2011)Google Scholar
  64. 64.
    Leung, C.K., Kononov, V.V., Pazdor, A.G.M., Jiang, F.: PyramidViz: visual analytics and big data visualization for frequent patterns. In: 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, 14th International Conference on Pervasive Intelligence and Computing, 2nd International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress, DASC/PiCom/DataCom/CyberSciTech 2016, Auckland, New Zealand, 8–12 August 2016, pp. 913–916. IEEE Computer Society (2016)Google Scholar
  65. 65.
    Leung, C.K.-S., Jiang, F.: RadialViz: an orientation-free frequent pattern visualizer. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 322–334. Springer, Berlin (2012)CrossRefGoogle Scholar
  66. 66.
    Liu, B., Hsu, W., Chen, S., Ma, Y.: Analyzing the subjective interestingness of association rules. IEEE Intell. Syst. 15(5), 47–55 (2000)CrossRefGoogle Scholar
  67. 67.
    Liu, Z., Kerr, B., Dontcheva, M., Grover, J., Hoffman, M., Wilson, A.: Coreflow: extracting and visualizing branching patterns from event sequences. Comput. Graph. Forum 36(3), 527–538 (2017)CrossRefGoogle Scholar
  68. 68.
    Liu, Z., Wang, Y., Dontcheva, M., Hoffman, M., Walker, S., Wilson, A.: Patterns and sequences: interactive exploration of clickstreams to understand common visitor paths. IEEE Trans. Vis. Comput. Graph. 23(1), 321–330 (2017)CrossRefGoogle Scholar
  69. 69.
    Mabroukeh, N.R., Ezeife, C.I.: A taxonomy of sequential pattern mining algorithms. ACM Comput. Surv. 43(1), 3:1–3:41 (2010)CrossRefGoogle Scholar
  70. 70.
    Mackinlay, J.D.: Automating the design of graphical presentations of relational information. ACM Trans. Graph. 5(2), 110–141 (1986)CrossRefGoogle Scholar
  71. 71.
    Mannila, H., Meek, C.: Global partial orders from sequential data. In: Ramakrishnan, R., Stolfo, S.J., Bayardo, R.J., Parsa, I. (eds.) Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, MA, USA, 20–23 August 2000, pp. 161–168. ACM (2000)Google Scholar
  72. 72.
    Mannila, H., Toivonen, H., Verkamo, A.I.: Discovery of frequent episodes in event sequences. Data Min. Knowl. Discov. 1(3), 259–289 (1997)CrossRefGoogle Scholar
  73. 73.
    Monroe, M., Lan, R., Lee, H., Plaisant, C., Shneiderman, B.: Temporal event sequence simplification. IEEE Trans. Vis. Comput. Graph. 19(12), 2227–2236 (2013)CrossRefGoogle Scholar
  74. 74.
    Munzner, T.: Visualization Analysis and Design. A.K. Peters Visualization Series. A K Peters, Natick (2014)CrossRefGoogle Scholar
  75. 75.
    Munzner, T., Kong, Q., Ng, R.T., Lee, J., Klawe, J., Radulovic, D., Leung, C.K.: Visual mining of power sets with large alphabets. Department of Computer Science, The University of British Columbia (2005)Google Scholar
  76. 76.
    Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Efficient mining of association rules using closed itemset lattices. Inf. Syst. 24(1), 25–46 (1999)zbMATHCrossRefGoogle Scholar
  77. 77.
    Patnaik, D., Butler, P., Ramakrishnan, N., Parida, L., Keller, B.J., Hanauer, D.A.: Experiences with mining temporal event sequences from electronic medical records: initial successes and some challenges. In: Apté, C., Ghosh, J., Smyth, P. (eds.) Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, 21–24 August 2011, pp. 360–368. ACM (2011)Google Scholar
  78. 78.
    Perer, A., Wang, F.: Frequence: interactive mining and visualization of temporal frequent event sequences. In: Kuflik, T., Stock, O., Chai, J.Y., Krüger, A. (eds.) 19th International Conference on Intelligent User Interfaces, IUI 2014, Haifa, Israel, 24–27 February 2014, pp. 153–162. ACM (2014)Google Scholar
  79. 79.
    Perer, A., Wang, F., Hu, J.: Mining and exploring care pathways from electronic medical records with visual analytics. J. Biomed. Inform. 56, 369–378 (2015)CrossRefGoogle Scholar
  80. 80.
    Rainsford, C.P., Roddick, J.F.: Visualisation of temporal interval association rules. In: Leung, K., Chan, L., Meng, H. (eds.) Intelligent Data Engineering and Automated Learning - IDEAL 2000, Data Mining, Financial Engineering, and Intelligent Agents, Second International Conference, Shatin, N.T. Hong Kong, China, 13–15 December 2000, Proceedings. Lecture Notes in Computer Science, vol. 1983, pp. 91–96. Springer, Berlin (2000)CrossRefGoogle Scholar
  81. 81.
    Sacha, D., Stoffel, A., Stoffel, F., Kwon, B.C., Ellis, G.P., Keim, D.A.: Knowledge generation model for visual analytics. IEEE Trans. Vis. Comput. Graph. 20(12), 1604–1613 (2014)CrossRefGoogle Scholar
  82. 82.
    Shneiderman, B.: Tree visualization with tree-maps: 2-d space-filling approach. ACM Trans. Graph. 11(1), 92–99 (1992)zbMATHCrossRefGoogle Scholar
  83. 83.
    Shneiderman, B.: The eyes have it: a task by data type taxonomy for information visualizations. In: VL, pp. 336–343. IEEE Computer Society (1996)Google Scholar
  84. 84.
    Silva, S.F., Catarci, T.: Visualization of linear time-oriented data: a survey. In: WISE, pp. 310–319. IEEE Computer Society (2000)Google Scholar
  85. 85.
    Stasko, J.T., Zhang, E.: Focus+context display and navigation techniques for enhancing radial, space-filling hierarchy visualizations. In: INFOVIS, pp. 57–65. IEEE Computer Society (2000)Google Scholar
  86. 86.
    Stolper, C.D., Perer, A., Gotz, D.: Progressive visual analytics: user-driven visual exploration of in-progress analytics. IEEE Trans. Vis. Comput. Graph. 20(12), 1653–1662 (2014)CrossRefGoogle Scholar
  87. 87.
    Viégas, F.B., Wattenberg, M., Feinberg, J.: Participatory visualization with wordle. IEEE Trans. Vis. Comput. Graph. 15(6), 1137–1144 (2009)CrossRefGoogle Scholar
  88. 88.
    Vrotsou, K., Johansson, J., Cooper, M.D.: ActiviTree: interactive visual exploration of sequences in event-based data using graph similarity. IEEE Trans. Vis. Comput. Graph. 15(6), 945–952 (2009)CrossRefGoogle Scholar
  89. 89.
    Wanner, F., Jentner, W., Schreck, T., Stoffel, A., Sharalieva, L., Keim, D.A.: Integrated visual analysis of patterns in time series and text data - workflow and application to financial data analysis. Inf. Vis. 15(1), 75–90 (2016)CrossRefGoogle Scholar
  90. 90.
    Wattenberg, M.: Arc diagrams: visualizing structure in strings. In: INFOVIS, pp. 110–116. IEEE Computer Society (2002)Google Scholar
  91. 91.
    Wei, J., Shen, Z., Sundaresan, N., Ma, K.: Visual cluster exploration of web clickstream data. In: IEEE VAST, pp. 3–12. IEEE Computer Society (2012)Google Scholar
  92. 92.
    Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann (2016)Google Scholar
  93. 93.
    Wong, P.C., Whitney, P., Thomas, J.: Visualizing association rules for text mining. In: IEEE Symposium on Information Visualization 1999 (INFOVIS’99), San Francisco, California, USA, 24–29 October 1999, pp. 120–123. IEEE Computer Society (1999)Google Scholar
  94. 94.
    Wongsuphasawat, K., Lin, J.J.: Using visualizations to monitor changes and harvest insights from a global-scale logging infrastructure at twitter. In: IEEE VAST, pp. 113–122. IEEE Computer Society (2014)Google Scholar
  95. 95.
    Wongsuphasawat, K., Gómez, J.A.G., Plaisant, C., Wang, T.D., Taieb-Maimon, M., Shneiderman, B.: LifeFlow: visualizing an overview of event sequences. In: CHI, pp. 1747–1756. ACM (2011)Google Scholar
  96. 96.
    Wu, S., Chen, Y.: Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events. Data Knowl. Eng. 68(11), 1309–1330 (2009)CrossRefGoogle Scholar
  97. 97.
    Yang, J., Ward, M.O., Rundensteiner, E.A., Patro, A.: Interring: a visual interface for navigating and manipulating hierarchies. Inf. Vis. 2(1), 16–30 (2003)CrossRefGoogle Scholar
  98. 98.
    Yang, L.: Visualizing frequent itemsets, association rules, and sequential patterns in parallel coordinates. In: ICCSA (1). Lecture Notes in Computer Science, vol. 2667, pp. 21–30. Springer, Berlin (2003)CrossRefGoogle Scholar
  99. 99.
    Yang, L.: Pruning and visualizing generalized association rules in parallel coordinates. IEEE Trans. Knowl. Data Eng. 17(1), 60–70 (2005)MathSciNetCrossRefGoogle Scholar
  100. 100.
    Zaki, M.J., Hsiao, C.: CHARM: an efficient algorithm for closed itemset mining. In: SDM, pp. 457–473. SIAM (2002)Google Scholar
  101. 101.
    Zhao, J., Liu, Z., Dontcheva, M., Hertzmann, A., Wilson, A.: MatrixWave: visual comparison of event sequence data. In: CHI, pp. 259–268. ACM (2015)Google Scholar
  102. 102.
    Zhao, Q., Bhowmick, S.S.: Sequential pattern mining: a survey. ITechnical Report CAIS Nayang Technological University Singapore, pp. 1–26 (2003)Google Scholar

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Authors and Affiliations

  1. 1.Universität KonstanzKonstanzGermany

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