Services Recommendation in Systems Based on Service Oriented Architecture by Applying Modified ROCK Algorithm

  • Agnieszka Prusiewicz
  • Maciej Zięba
Part of the Communications in Computer and Information Science book series (CCIS, volume 88)


In this work the proposal for services recommendation in online educational systems based on service oriented architecture is introduced. The problem of recommending services responsible for creating student groups are taken into account and as the criterion of the grouping the student learning potential is considered. As a method of grouping modified ROCK algorithm is used during service execution.


e-education ROCK clustering students SOA 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aggarwal, C., Wolf, J., Yu, P., Park, J.: Fast algorithms for projected clustering. In: Proceedings of the ACM SIGMOD Conference, Philadelphia (1996)Google Scholar
  2. 2.
    Ankerst, M., Breunig, M., Kriegel, H., Sander, J.: OPTICS: Ordering points to identify clustering structure. In: Proceedings of the ABM SIGMOD Conference, Philadelphia (1999)Google Scholar
  3. 3.
    Berkhin, P.: Survey of Clustering Data Mining Techniques. Accrue Software, San Jose (2003)Google Scholar
  4. 4.
    Ganti, V., Ramakrishnan, R., Gehrke, J.: CACTUS – Clustering Categorical Data Using Summaries. In: Proceedings of the 5th ACM SIGKDD, San Diego, pp. 78–83 (1999)Google Scholar
  5. 5.
    Gibson, D., Kleinberg, J., Raghavan, P.: Clustering categorical data: An approach based on dynamical systems. In: International Conference on Very Large Databases, New York (1998)Google Scholar
  6. 6.
    Guha, S., Rastogi, R., Shim, K.: CURE: An Efficient Clustering Algorithm for Large Databases. In: Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data, Seattle, vol. 27(2), pp. 73–84 (1998)Google Scholar
  7. 7.
    Guha, S., Rastogi, R., Shim, K.: ROCK: a robust clustering algorithm for categorical attributes. In: Proceedings of the 15th International Conference on Data Engineering, Sydney (1999)Google Scholar
  8. 8.
    Karypis, G., Han, E.H., Kumar, V.: CHAMELEON: a hierarchical clustering algorithm using dynamic modeling. IEEE Comput. (1999)Google Scholar
  9. 9.
    Marques De Sa, J.: Pattern Recognition – Concepts, Methods and Applications. Springer, Oporto University, Portugal (2001)Google Scholar
  10. 10.
    Mercik, J., Szmigiel, J.: Econometry. WSZiF, Wroclaw (2000)Google Scholar
  11. 11.
    Mingers, J., O’Brien, F.: Creating Student Groups with Similar Characteristics: A Heuristic Approach. Omega, Int. J. Mgmt. Sci. 23(3), 313–321 (1995)CrossRefGoogle Scholar
  12. 12.
    Mor, E., Minguillon, J.: E-learning personalization based on itineraries and long-term navigational behavior. In: Proceedings of the 13th International World Wide Web Conference, pp. 264–265 (2004)Google Scholar
  13. 13.
    Romero, C., Ventura, S.: Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications 33, 135–146 (2007)CrossRefGoogle Scholar
  14. 14.
    Sandler, J., Ester, M., Kriegel, H., Xu, X.: Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applictions. Data Mining and Knowledge Discovery 2 (1998)Google Scholar
  15. 15.
    Talavera, L., Gaudioso, E.: Mining student data to characterize similar behavior groups in unstructured collaboration spaces. In: Workshop on Artificial Intelligence in CSCL. 16th European Conference on Artificial Intelligence, pp. 17–23 (2004)Google Scholar
  16. 16.
    Tang, T.Y., Chan, K.C.: Feature Construction for Student Group Forming Based on Their Browsing Behaviors in an E-Learning System. PRICAI, Hong Kong (2002)Google Scholar
  17. 17.
  18. 18.
    Witten, I.H., Frank, E.: Data Mining. In: Practical Machine Learning Tools and Techniques. Elsevier, San Francisco (2005)Google Scholar
  19. 19.
    Xu, X., Ester, M., Kriegel, H.P., Sander, J.: A distribution-based clustering algorithm for mining in large spatial databases. In: Proceedings of the 14th ICDE, Orlando, FL (1998)Google Scholar
  20. 20.
    Zhango, T., Ramakrishnan, R., Livny, M.: BIRCH: An Efficient Data Clustering Method for Very Large Databases. In: International Conference on Management of Data, vol. 25(2), pp. 103–114 (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Agnieszka Prusiewicz
    • 1
  • Maciej Zięba
    • 1
  1. 1.Institute of Informatics, Faculty of Computer Science and, ManagementWroclaw University of TechnologyWrocławPoland

Personalised recommendations