Knowledge and Information Systems

, Volume 8, Issue 3, pp 257–275 | Cite as

Visualising hierarchical associations

  • Aaron Ceglar
  • John Roddick
  • Paul Calder
  • Chris Rainsford
Article

Abstract

Recent association-mining research has led to the development of techniques that allow the accommodation of concept hierarchies within the mining process. This extension results in the discovery of rules which associate not only groups of items but which are also influenced by the hierarchies within which an item may reside. Given this, there then arises a need for techniques whereby such hierarchical associations can be presented to the user. Current association rule visualisation techniques are limited, as they do not effectively incorporate or enable the visualisation of hierarchical semantics. This paper presents a review of current hierarchical and association visualisation techniques and introduces a novel technique for visualising hierarchical association rules.

Keywords

Knowledge discovery Association mining Visualisation Concept abstraction 

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References

  1. 1.
    Adamo J (2001) Data mining association rules and sequential patterns: sequential and parallel algorithms. Springer, Berlin Heidelberg New YorkGoogle Scholar
  2. 2.
    Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In: 1993 ACM SIGMOD international conference on management of data, Washington, DC, pp 207–216Google Scholar
  3. 3.
    Anderson D, Anderson E, Lesh N, Marks J, Perlin K, Ratajczak D, Ryall K (2000) Human guided simple search: combining information visualization and heuristic search. In: Proceedings of the workshop on new paradigms in information visualization and manipulation, in conjunction with the 8th ACM international conference on information and knowledge management. ACM, Kansas City, MO, pp 21–25Google Scholar
  4. 4.
    Baker CAH, Carpendale MST, Prusinkiewicz P, Surette MG (2002) Genevis: visualization tools for genetic regulatory network dynamics. In: Proceedings of the conference on visualization’02. IEEE, Boston, MA, pp 243–250Google Scholar
  5. 5.
    Battista GD, Eades P, Tamassia R, Tollis IG (1999) Graph drawing: algorithms for the visualization of graphs. Prentice Hall, Englewood Cliffs, NJGoogle Scholar
  6. 6.
    Boardman R (2000) Bubble trees: visualization of hierarchical information trees. In: ACM CHI’00 conference on human factors extended abstracts. ACM, The HagueGoogle Scholar
  7. 7.
    Brin S, Page L (1999) Dynamic data mining: exploring large rule spaces by sampling, Stanford UniversityGoogle Scholar
  8. 8.
    Carriere J, Kazman R (1995) Interacting with huge hierarchies: beyond cone trees. In: InfoViz’95, IEEE symposium on information visualisation. IEEE Computer Society, Atlanta, GA, pp 74–78Google Scholar
  9. 9.
    Ceglar A (2003) Hierarchical association algorithms: a survey. Technical Report SIE-03-001, Flinders University, Adelaide, South AustraliaGoogle Scholar
  10. 10.
    Ceglar A, Roddick J, Calder P (2003) Guiding knowledge discovery through interactive data mining. In: Pendharker P (ed) Managing data mining technologies in organisations: techniques and applications. IDEA Group, Hershey, PA, pp 45–90Google Scholar
  11. 11.
    Chapman P, Kerber R, Clinton J, Khabaza T, Reinartz TP, Wirth R (1999) The CRISP-DM process model. Discussion paper, CRISP-DM ConsortiumGoogle Scholar
  12. 12.
    Eades P (1992) Drawing free trees. Bull Inst Combinatorics Appl 5:10–36Google Scholar
  13. 13.
    Han J, Fu Y (1994) Dynamic generation and refinement of concept hierarchies for knowledge discovery in databases. In: Proceedings of the AAAI’94 workshop on knowledge discovery in databases (KDD94), Seattle, WA, pp 157–168Google Scholar
  14. 14.
    Han J, Fu Y (1995) Discovery of multiple-level association rules from large databases. In: 21st international conference on very large databases, Zurich, SwitzerlandGoogle Scholar
  15. 15.
    Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. In: Chen W, Naughton J, Bernstein P (eds) 2000 ACM SIGMOD international conference on management of data. ACM, New York, NY, pp 1–12Google Scholar
  16. 16.
    Han J, Kamber M, Tung AKH (2001) Spatial clustering methods in data mining: a survey. In: Miller H, Han J (eds) Geographic data mining and knowledge discovery. Taylor and Francis, LondonGoogle Scholar
  17. 17.
    Hao MC, Dayal U, Hsu M, Sprenger T, Gross MH (2001) Visualization of directed associations in e-commerce transaction data. In: Proceedings of VisSym’01, joint Eurographics–IEEE TCVG symposium on visualization. IEEE, Ascona, Switzerland, pp 185–192Google Scholar
  18. 18.
    Herman I, Melancon G, Marshall MS (2000a) Graph visualisation and navigation in information visualization: a survey. IEEE Trans Visualiz Comput Graph 6(1):24–43CrossRefGoogle Scholar
  19. 19.
    Herman I, Melancon G, Ruiter MMD, Delest M (2000b) Latour—a tree visualization system. Lecture notes in computer science, vol 1731. Springer, Berlin Heidelberg New York, pp 392–404Google Scholar
  20. 20.
    Hetzler B, Harris WM, Havre S, Whitney P (1998) Visualizing the full spectrum of document relationships. In: Proceedings of the 5th international ISKO conference, San Francisco, CA, pp 168–175Google Scholar
  21. 21.
    Hipp J, Myka A, Wirth R, Guntzer U (1998) A new algorithm for faster mining of generalised association rules. In: Proceedings of the 2nd symposium on principles of data mining and knowledge discovery (PKDD’98), Nantes, France, pp 74–82Google Scholar
  22. 22.
    Hofman H, Siebes AP, Wilhelm AF (2000) Visualizing association rules with interactive mosaic plots. In: KDD 2000, ACM, Boston, MA, pp 227–235Google Scholar
  23. 23.
    Johnson B, Schneiderman B (1991) Tree-maps: a space-filling approach to the visualization of hierarchical information structures. In: IEEE visualization’91. Wiley-IEEE Computer Society, Indianapolis, IN, pp 275–282Google Scholar
  24. 24.
    KDM (n.d.) Data repository. KDM Lab, School of Informatics and Engineering, Flinders University. http://kdm.first.flinders.edu.au/IDM/Google Scholar
  25. 25.
    Klemettinen M, Mannila H, Ronkainen T, Verkano A (1994) Finding interesting rules from large sets of discovered association rules. In: Adam NR, Bhargava BK, Yesha Y (eds) Third international conference on information and knowledge management (CIKM’94). ACM, Gaithersburg, MD, pp 401–407Google Scholar
  26. 26.
    Koike H, Yoshihara H (1993) Fractal approaches for visualizing huge hierarchies. In: Gilbert EP, Olsen KA (eds) IEEE symposium on visual languages VL’93. IEEE Computer Society, pp 55–60Google Scholar
  27. 27.
    Kreuseler M, Schuman H (1999) Information visualization using a new focus + context technique in combination with dynamic clustering of information space. In: New paradigms in information visualization and manipulation, Kansas City, MO, pp 1–5Google Scholar
  28. 28.
    Lesh N, Marks J, Patrignani M (2000) Interactive partitioning. Technical report, Mitsubishi Electronic Research Laboratory, Cambridge, MAGoogle Scholar
  29. 29.
    Mao R (2001) Adaptive-FP: an efficient and effective method for multi-level multi-dimensional frequent pattern mining. Thesis, Simon Fraser University, Burnaby, CanadaGoogle Scholar
  30. 30.
    Miller GA (1956) The magic number seven, plus or minus two: some limits on our capacity for processing information. Psychol Rev 63:81–97PubMedGoogle Scholar
  31. 31.
    Ong K-H, Ong K-L, Ng W-K, Lim E-P (2002) CrystalClear: active visualization of association rules. In: International workshop on active mining (AM-2002) in conjunction with the IEEE international conference on data mining (ICDN’02). Maebashi City, JapanGoogle Scholar
  32. 32.
    Ong K-L, Ng W-K, Lim E-P (2001) Large mining multi-level rules with recurrent items using fp-tree. In: Proceedings of the 3rd IEEE conference on information, communications and signal processing (ICICS’2001). SingaporeGoogle Scholar
  33. 33.
    Purchase HC (1998) Which aesthetic has the greatest effect upon human understanding? In: Proceedings of the symposium on graph drawing GD’97. Springer, Berlin Heidelberg New York, pp 248–261Google Scholar
  34. 34.
    Rainsford C, Roddick J (2000) Visualisation of temporal interval association rules. In: Proceedings of the 2nd international conference on intelligent data engineering and automated learning. Shatin, NT, Hong Kong, pp 91–96Google Scholar
  35. 35.
    Rathjens D (1997), MineSet users guide. Technical report, Silicon Graphics, Inc., CAGoogle Scholar
  36. 36.
    Robertson GG, Mackinley JD, Card SS (1991) Cone trees: animated 3d visualisations of hierarchical information. In: CHI’91, pp 189–194Google Scholar
  37. 37.
    Sindre G, Gulla B, Jokstad G (1993) Onion graphs: aesthetics and layout. In: IEEE/CS symposium on visual languages, Wiley-IEEE Computer Society, Indianapolis, IN, pp 287–291Google Scholar
  38. 38.
    Srikant R, Agrawal R (1997) Mining generalized association rules. Future Generation Comput Syst 13(2–3):161–180Google Scholar
  39. 39.
    Srikant R, Vu Q, Agrawal R (1997) Mining association rules with item constraints. In: Eckerman D, Mannila H, Pregibon D, Uthursamy R (eds) 3rd international conference on knowledge discovery and data mining. AAAI, Newport Beach, CA, pp 67–73Google Scholar
  40. 40.
    Thomas S, Sarawagi S (1998) Mining generalized association rules and sequential patterns using sql queries. In: Proceedings of the 4th international conference on knowledge discovery and data mining (KDD’98). New York, NY, pp 344–348Google Scholar
  41. 41.
    van Wijk JJ, van de Wetering H (1999) Cushion treemaps: visualization of hierarchical information. In: IEEE symposium on information visualization INFOVIS’99, pp 73–78Google Scholar
  42. 42.
    Wetherell C, Shannon A (1979) Tidy drawing of trees. IEEE Trans Softw Eng 5(5):514–520Google Scholar
  43. 43.
    Wong PC, Whitney P, Thomas J (1999) Visualizing association rules for text mining. In: Proceedings of IEEE symposium on information visualization’99, IEEE Computer Society. Los Alamitos, CA, pp 120–124Google Scholar
  44. 44.
    Yee KP, Fisher D, Dhamija R, Hearst M (2001) Animated exploration of graphs with radial layout. In: IEEE symposium on information visualisation 2001, InfoVis’01, pp 43–50Google Scholar

Copyright information

© Springer-Verlag 2004

Authors and Affiliations

  • Aaron Ceglar
    • 1
  • John Roddick
    • 1
  • Paul Calder
    • 1
  • Chris Rainsford
    • 1
    • 2
  1. 1.School of Informatics and EngineeringFlinders University of South AustraliaAdelaideAustralia
  2. 2.CSIRO Mathematical and Information SciencesCanberraAustralia

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