Skip to main content
SpringerLink
Log in

Determination of user interfaces in adaptive systems using a rough classification-based method

  • Regular Papers
  • Published:
New Generation Computing Aims and scope Submit manuscript

Abstract

This paper presents a novel method for user classification in adaptive systems based on rough classification. Adaptive systems could be used in many areas, for example in a user interface construction or e-Learning environments for learning strategy selection. In this paper the adaptation of web-based system user interface is presented. The goal of rough user classification is to select the most essential attributes and their values that group together users who are very much alike concerning the system logic. In order to group users we exploit their usage data taken from the user model of the adaptive web-based system user interface. We presented three basic problems for attribute selection that generates the following partitions: that is included, that includes and that is the closest to the given partition.

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. Arafa, Y. and Mamdani, A., “Virtual Personal Service Assistants: Towards Real-time Characters with Artificial Hearts,”Proc. Intelligent User Interfaces, New Orleans, LA, USA, pp. 9–12, 2000.

  2. Basili R., Pazienza M.T. and Zanzotto, F.M., “Exploiting the Feature Vector Model for Learning Linguistic Representations of Relational Concepts,”Proc. of the International Workshop, Tutorial on Adaptive Text Extraction and Mining held in conjunction with the 14th European Conference on Machine Learning and the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, 22 September, 2003, Cavtat-Dubrovnik, Croatia, pp. 2–10, 2003.

  3. Boudjeloud, L. and Poulet, F., “Attribute Selection for High Dimensional Data Clustering,”Proc. of the Conference on Applied Stochastic Models and Data Analysis (ASMDA 2005), pp. 387–394, 2005. http://asmda2005.enst-bretagne.fr/IMG/pdf/proceedings/387.pdf

  4. Dattola, R.T.,A Fast Algorithm for Automatic Classification, Report ISR-14 to the National Science Foundation, Section V, Cornell University, Department of Computer Science, 1968.

  5. Hernandez, F., Gaudioso, E. and Boticario, J.G., “A Multi-agent Approach to Obtain Open and Flexible User Models in Adaptive Learning Communities,”LNAI, 2702, pp. 203–207, 2003.

    Google Scholar 

  6. International Standard ISO 9241-11, “Ergonomic Requirements for Office Work with Visual Display Terminals (VDT’s) — Part 11: Guidance on Usability,” 1997.

  7. Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C., Silverman, R. and Wu, A.Y., “An Efficient K-means Clustering Algorithm: Analysis and Implementation,”IEEE Transactions On Pattern Analysis And Machine Intelligence, 24, 7, pp. 881–892, 2002.

    Article  Google Scholar 

  8. Kobsa, A., Koenemann, J. and Pohl, W., “Personalized Hypermedia Presentation Techniques for Improving Online Customer Relationships,”The Knowledge Engineering Review, 16, 2, pp. 111–155, 2001.

    Article  MATH  Google Scholar 

  9. Kobsa, A., “Personalized Hypermedia and International Privacy,”Communications of the ACM, 45, 5, pp. 64–67, 2002.

    Article  Google Scholar 

  10. Lieberman, H., “Autonomous Interface Agents,”Proc. CHI, 97, ACM, pp. 67–74, 1997.

    Google Scholar 

  11. Montaner, M., Lopez, B., De La Rosa, J.L., “A Taxonomy of Recommender Agents on the Internet,”Artificial Intelligence Review, 19, pp. 285–330, 2003.

    Article  Google Scholar 

  12. Musial, K. and Nguyen, N.T., “On the Nearest Product of Partitions,”Bull. of Polish Academy of Sci., 36, 5–6, pp. 333–338, 1989.

    Google Scholar 

  13. Nguyen, N.T., “Consensus System for Solving Conflicts in Distributed Systems,”Information Sciences — An International Journal, 147, pp. 91–122, 2002.

    MATH  MathSciNet  Google Scholar 

  14. Nguyen, N.T. and Sobecki, J., “Using Consensus Methods to Construct Adaptive Interfaces in Multimodal Web-based Systems,”Universal Access in Information Society, 2, 4, pp. 342–358, 2003.

    Article  Google Scholar 

  15. Pantel, P and Lin, D., “Document Clustering with Committees,”Proc. of ACM SIGIR 2002, Tampere, Finland, pp. 199–206, 2002.

  16. Papatheodorou, C., “Machine Learning in User Modeling,”Machine Learning and Its Applications, pp. 286–294, 2001.

  17. Pawlak Z., “Information Systems — Theoretical foundations,”Information Systems, 6, pp. 205–218, 1981.

    Article  MATH  Google Scholar 

  18. Pawlak, Z., “Rough Classification,”Int. J. Human-Computer Studies, 51, pp. 369–383, 1999.

    Article  Google Scholar 

  19. Schneiderman, B. and Maes, P., “Debate: Direct Manipulation vs. Interface Agents,”Interactions, 4, 6, pp. 42–61, 1997.

    Article  Google Scholar 

  20. Shneiderman, B., “Universal Usability,”Communication of the ACM, 43, 5, pp. 85–91, 2000.

    Article  Google Scholar 

  21. Sobecki, J. and Nguyen, N.T., “Using Consensus Methods to User Classification in Interactive Systems,”Advances in Soft Computing, Soft Methods in Probability, Statistics and Data Analysis (Grzegorzewski, P., Hryniewicz, O. and Gil, M.A. Eds.), Physica-Verlag (Springer Group), Heidelberg; New York, pp. 346–354, 2002.

    Google Scholar 

  22. Sobecki J., “XML-based Interface Model Socially Adaptive Web-Based Systems User Interfaces,”LNCS, 2660, pp. 592–598, 2003.

    Google Scholar 

  23. Sobecki J. and Weihberg M., “Consensus-based Adaptive User Interface Implementation in Product Promotion,”Designing a More Inclusive World (Keates S. et al.), pp. 111–121, Springer 2004.

  24. Stephanidis, C. and Savidis, A., “Universal Access in the Information Society: Methods, Tools, and Interaction Technologies,”Universal Access in Information Society, 1, pp. 40–55, 2001.

    Google Scholar 

  25. http://rss.acs.unt.edu/Rdoc/library/arules/html/Adult.html

Download references

Author information

Authors and Affiliations

  1. Wroclaw University of Technology, Wyb. Wyspianskiego 27, 50-370, Wroclaw, Poland

    Ngoc Thanh Nguyen & Janusz Sobecki

Authors
  1. Ngoc Thanh Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Janusz Sobecki
    View author publications

    You can also search for this author in PubMed Google Scholar

Additional information

Ngoc Thanh Nguyen, Ph.D., D.Sc.: He currently works as an associate professor at the Faculty of Computer Science and Management, Wroclaw University of Technology in Poland. He received his diplomas of M.Sc, Ph.D. and D.Sc. in Computer Science in 1986, 1989 and 2002, respectively. Actually, he is working on intelligent technologies for conflict resolution and inconsistent knowledge processing and e-learning methods. His teaching interests consist of database systems and distributed systems. He is a co-editor of 4 special issues in international journals, author of 3 monographs, editor of one book and about 110 other publications (book chapters, journal and refereed conference papers). He is an associate editor of the following journals: “International Journal of Computer Science & Application”; “Journal of Information Knowledge System Management”; and “International Journal of Knowledge-Based & Intelligent Engineering Systems”. He is a member of societies: ACM, IFIP WG 7.2, ISAI, KES International, and WIC.

Janusz Sobecki, Ph.D.: He is an Assistant Professor in Institute of Applied Informatics (IAI) at Wroclaw University of Technology (WUT). He received his M. Sc. in Computer Science from Faculty of Computer Science and Management at WUT in 1986 and Ph.D. in Computer Science from Faculty of Electronics at WUT in 1994. For 1986–1996 he was an Assistant at the Department of Information Systems (DIS) at WUT. For 1988–1996 he was also a head of the laboratory at DIS. For 1996–2004 he was an Assistant Professor in DIS and since fall of 2004 at IAI, both at WUT. His research interests include information retrieval, multimedia information systems, system usability and recommender systems. He is on the editorial board of New Generation Computing and was a co-editor of two journal special issues. He is a member of American Association of Machinery.

About this article

Cite this article

Nguyen, N.T., Sobecki, J. Determination of user interfaces in adaptive systems using a rough classification-based method. New Gener Comput 24, 377–402 (2006). https://doi.org/10.1007/BF03037400

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF03037400

Keywords

Search

Navigation