Skip to main content
SpringerLink
Log in

Visualising hierarchical associations

  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Adamo J (2001) Data mining association rules and sequential patterns: sequential and parallel algorithms. Springer, Berlin Heidelberg New York

    Google Scholar 

  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 207–216

  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 21–25

  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 243–250

  5. Battista GD, Eades P, Tamassia R, Tollis IG (1999) Graph drawing: algorithms for the visualization of graphs. Prentice Hall, Englewood Cliffs, NJ

    Google Scholar 

  6. Boardman R (2000) Bubble trees: visualization of hierarchical information trees. In: ACM CHI’00 conference on human factors extended abstracts. ACM, The Hague

  7. Brin S, Page L (1999) Dynamic data mining: exploring large rule spaces by sampling, Stanford University

    Google Scholar 

  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 74–78

  9. Ceglar A (2003) Hierarchical association algorithms: a survey. Technical Report SIE-03-001, Flinders University, Adelaide, South Australia

    Google Scholar 

  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 45–90

  11. Chapman P, Kerber R, Clinton J, Khabaza T, Reinartz TP, Wirth R (1999) The CRISP-DM process model. Discussion paper, CRISP-DM Consortium

  12. Eades P (1992) Drawing free trees. Bull Inst Combinatorics Appl 5:10–36

    Google Scholar 

  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 157–168

  14. Han J, Fu Y (1995) Discovery of multiple-level association rules from large databases. In: 21st international conference on very large databases, Zurich, Switzerland

  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 1–12

  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, London

  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 185–192

  18. Herman I, Melancon G, Marshall MS (2000a) Graph visualisation and navigation in information visualization: a survey. IEEE Trans Visualiz Comput Graph 6(1):24–43

    Article  Google Scholar 

  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 392–404

  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 168–175

  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 74–82

  22. Hofman H, Siebes AP, Wilhelm AF (2000) Visualizing association rules with interactive mosaic plots. In: KDD 2000, ACM, Boston, MA, pp 227–235

  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 275–282

  24. KDM (n.d.) Data repository. KDM Lab, School of Informatics and Engineering, Flinders University. http://kdm.first.flinders.edu.au/IDM/

  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 401–407

  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 55–60

  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 1–5

  28. Lesh N, Marks J, Patrignani M (2000) Interactive partitioning. Technical report, Mitsubishi Electronic Research Laboratory, Cambridge, MA

  29. Mao R (2001) Adaptive-FP: an efficient and effective method for multi-level multi-dimensional frequent pattern mining. Thesis, Simon Fraser University, Burnaby, Canada

  30. Miller GA (1956) The magic number seven, plus or minus two: some limits on our capacity for processing information. Psychol Rev 63:81–97

    PubMed  Google Scholar 

  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, Japan

  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). Singapore

  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 248–261

  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 91–96

  35. Rathjens D (1997), MineSet users guide. Technical report, Silicon Graphics, Inc., CA

  36. Robertson GG, Mackinley JD, Card SS (1991) Cone trees: animated 3d visualisations of hierarchical information. In: CHI’91, pp 189–194

  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 287–291

  38. Srikant R, Agrawal R (1997) Mining generalized association rules. Future Generation Comput Syst 13(2–3):161–180

    Google Scholar 

  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 67–73

  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 344–348

  41. van Wijk JJ, van de Wetering H (1999) Cushion treemaps: visualization of hierarchical information. In: IEEE symposium on information visualization INFOVIS’99, pp 73–78

  42. Wetherell C, Shannon A (1979) Tidy drawing of trees. IEEE Trans Softw Eng 5(5):514–520

    Google Scholar 

  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 120–124

  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 43–50

Download references

Author information

Authors and Affiliations

  1. School of Informatics and Engineering, Flinders University of South Australia, Adelaide, Australia

    Aaron Ceglar, John Roddick, Paul Calder & Chris Rainsford

  2. CSIRO Mathematical and Information Sciences, Canberra, Australia

    Chris Rainsford

Authors
  1. Aaron Ceglar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. John Roddick
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Paul Calder
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chris Rainsford
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to John Roddick.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ceglar, A., Roddick, J., Calder, P. et al. Visualising hierarchical associations. Knowl Inf Syst 8, 257–275 (2005). https://doi.org/10.1007/s10115-003-0139-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-003-0139-0

Keywords

Search

Navigation