CONTEXT OF USE ANALYSIS - Activity Checklist for Visual Data Mining

Edwige Fangseu Badjio, François Poulet



In this paper, emphasis is placed on understanding how human behaviour interacts with visual data mining (VDM) tools in order to improve their design and usefulness. Computer tools that are more useful assist users in achieving desired goals. Our objective is to highlight quality in context of use problems with existing VDM systems that need to be addressed in the design of new VDM systems. For this purpose, we defined a checklist based on activity theory. The responses provided by 15 potential users are summarized as design insights. The users respond to questions selected from the activity checklist. This paper describes the evaluation method and shares lessons learned from its application.


  1. Ankerst M., Elsen C., Ester M., Kriegel H.-P., 1999. Visual classification: An interactive approach to decision tree construction. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.392-396.
  2. Bastien J.M.C., Scapin D.L., Leulier C., 1999. The ergonomic criteria and the ISO/DIS 9241-10 dialogue principles: a pilot comparison in an evaluation task. In Interacting with Computers, vol. 11(3), pp.299-322.
  3. Bevan N., Macleod M., 1994. Usability measurement in context. In Behaviour & Information Technology, vol. 13(1-2), pp.132-145.
  4. Beyer H., Holtzblatt K., 1999. Contextual design. In ACM interactions, vol. 6(1), pp.32-49.
  5. Blake C., Merz C., 1998. UCI Repository of machine learning databases, [ pository.html]. Irvine, University of California, Department of Information and Computer Science.
  6. Cox K.C., Eick S.G., Wills G.J., Brachman R.J., 1997. Visual Data Mining: Recognizing Telephone Calling Fraud. In Data Mining and Knowledge Discovery, vol. 1, pp. 225-231.
  7. Deboeck G., Kohonen T., 1998. Visual Explorations in Finance with self organizing maps, Springer-Verlag.
  8. Dillon A., Morris M., 1966. User acceptance of information technology: theories and models. In M. Williams (ed.), Medford, NJ: Information Today, Vol. 31.
  9. Engeström, Y., 1987. Learning by Expanding: An ActivityTheoretical Approach to Developmental Research. Helsinki: Orienta-Konsultit Oy, Finland.
  10. Fangseu Badjio E., Poulet F., 2004a. A decision support system for data miners. In AISTA'04, International Conference on Advances in Intelligent Systems - Theory and Applications in cooperation with IEEE.
  11. Fangseu Badjio E., Poulet F., 2004b. Usability of Visual Data Mining Tools. In ICEIS'04, 6th International Conference on Enterprise Information Systems, vol.5, 254-258. ICEIS Press.
  12. Fangseu Badjio E., Poulet F., 2005a. Towards usable visual data mining environments. In HCII'05, 11th International Conference on Human-Computer Interaction.
  13. Fangseu Badjio E., Poulet F., 2005b. Visual data mining tools: quality metrics definition and application. In ICEIS'05, 7th International Conference on Enterprise Information Systems, vol. 5, pp.98-103. ICEIS press.
  14. Fayyad U. M., Piatetsky-Shapiro G., Smyth P., 1996. (ed) Advances in Knowledge Discovery and Data Mining. AAAI Press / MIT Press, Menlo Park, CA.
  15. Freitas A., Lavington S. H., 1998. Mining Very Large Databases with Parallel Processing Series, International Series on Advances in Database Systems, vol. 9.
  16. Grinstein G. G., Hoffman P., Laskowski S. J., Pickett R. M., 1997. Benchmark Development for the Evaluation of Visualization for Data Mining. In Issues in the Integration of Data Mining and Data Visualization, Workshop, Newport Beach, California.
  17. Grossman R. L., Yike Guo, 2002. Parallel Methods for Scaling Data Mining Algorithms to Large Data Sets. In Handbook on Data Mining and Knowledge Discovery, Jan M Zytkow, editor, pp.433-442. Oxford University Press.
  18. Hasan H., 2001. An Overview of Different Techniques for applying Activity Theory to Information Systems. In Information Systems and Activity Theory: Theory and Practice (Ed, Hasan, H.) University of Wollongong Press.
  19. Inselberg A., 1998. Visual Data Mining with Parallel Coordinates. In Computational Statistics Vol. 13(1), pp.47-63.
  20. ISO (International Organization for Standardization), 1998. ISO 13407: Human-Centered Design Process for Interactive Systems.
  21. Jinyan L., Huiqing L., 2005. Kent Ridge Bio-medical Data Set Repository., accessed the 2nd October 2005.
  22. Kaptelinin V., Nardi B. A., Macaulay C., 1999. The Activity Checklist: A Tool For Representing the "Space" of Context. Interactions, Vol.6, pp. 27-39.
  23. Keim D.A., 1996. Pixel-oriented Visualization Techniques for Exploring Very Large Databases. In Journal of Computational and Graphical Statistics, vol. 5(1), pp.58-77.
  24. Kuutti K., 1996. Activity Theory as a Potential Framework for Human-Computer Interaction Research. In Nardi, B.A., (1996) (Ed) Context and Consciousness: Activity Theory and Human-Computer Interaction. MIT Press.
  25. Leont'ev A. N., 1978. Activity, Consciousness, Personality. Englewood Cliffs, NJ, Prentice Hall.
  26. Maguire M., 2001. Context of use within usability activities. In International Journal Human-Computer Studies vol. 55.
  27. Marghescu D., Rajanen M., Back B., 2004. Evaluating the Quality of Use of Visual Data-Mining Tools. In ECITE'04, 11th European Conference on Information Technology Evaluation, pp. 239-250.
  28. Nardi B., (Ed.), 1996. Context and Consciousness. Activity Theory and Human Computer Interaction. MIT Press.
  29. Nielsen J., Landauer T. K., 1993. A mathematical model of the finding of usability problems. In INTERCHI'93, 4th International Conference on Human-Computer Interaction, pp. 206-213. ACM Press.
  30. Suchman L.A., 1987. Plans and situated actions: The problem of human-machine communication. Cambridge University Press.
  31. Whiteside J., Bennett J., Holtzblatt K., 1988. Usability engineering: our experience and evolution. In M. Helander, Ed. Handbook of Human Computer Interaction, pp.791-817. Amsterdam: Elsevier.
  32. Witten I. H., Eibe F., 2000. Data Mining: Practical machine learning tools with Java implementations. Morgan Kaufmann, San Francisco.
  33. Wolf C. G., 1989. The role of laboratory experiments in HCI: help, hindrance or Ho-hum?. In CHI'89, 6th conference on Human Factors in Computing Systems, pp.265-268. ACM Press.
  34. Yujin C., Qingyuan Z., Jianming W., 2004. Visual Data Mining Based on Parallel Coordinates and Rough Sets. In ICITA'04, 2nd International Conference on Information Technology for Application.

Paper Citation

in Harvard Style

Fangseu Badjio E. and Poulet F. (2006). CONTEXT OF USE ANALYSIS - Activity Checklist for Visual Data Mining . In Proceedings of the Eighth International Conference on Enterprise Information Systems - Volume 5: ICEIS, ISBN 978-972-8865-45-0, pages 45-50. DOI: 10.5220/0002456100450050

in Bibtex Style

author={Edwige Fangseu Badjio and François Poulet},
title={CONTEXT OF USE ANALYSIS - Activity Checklist for Visual Data Mining},
booktitle={Proceedings of the Eighth International Conference on Enterprise Information Systems - Volume 5: ICEIS,},

in EndNote Style

JO - Proceedings of the Eighth International Conference on Enterprise Information Systems - Volume 5: ICEIS,
TI - CONTEXT OF USE ANALYSIS - Activity Checklist for Visual Data Mining
SN - 978-972-8865-45-0
AU - Fangseu Badjio E.
AU - Poulet F.
PY - 2006
SP - 45
EP - 50
DO - 10.5220/0002456100450050