Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Aggarwal, C., Wolf, J., Yu, P., Park, J.: Fast algorithms for projected clustering. In: Proceedings of the ACM SIGMOD Conference, Philadelphia (1996)
Ankerst, M., Breunig, M., Kriegel, H., Sander, J.: OPTICS: Ordering points to identify clustering structure. In: Proceedings of the ABM SIGMOD Conference, Philadelphia (1999)
Berkhin, P.: Survey of Clustering Data Mining Techniques. Accrue Software, San Jose (2003)
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)
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)
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)
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)
Karypis, G., Han, E.H., Kumar, V.: CHAMELEON: a hierarchical clustering algorithm using dynamic modeling. IEEE Comput. (1999)
Marques De Sa, J.: Pattern Recognition – Concepts, Methods and Applications. Springer, Oporto University, Portugal (2001)
Mercik, J., Szmigiel, J.: Econometry. WSZiF, Wroclaw (2000)
Mingers, J., O’Brien, F.: Creating Student Groups with Similar Characteristics: A Heuristic Approach. Omega, Int. J. Mgmt. Sci. 23(3), 313–321 (1995)
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)
Romero, C., Ventura, S.: Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications 33, 135–146 (2007)
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)
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)
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)
WEKA SOFTWARE, http://www.cs.waikato.ac.nz/ml/weka/
Witten, I.H., Frank, E.: Data Mining. In: Practical Machine Learning Tools and Techniques. Elsevier, San Francisco (2005)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Prusiewicz, A., Zięba, M. (2010). Services Recommendation in Systems Based on Service Oriented Architecture by Applying Modified ROCK Algorithm. In: Zavoral, F., Yaghob, J., Pichappan, P., El-Qawasmeh, E. (eds) Networked Digital Technologies. NDT 2010. Communications in Computer and Information Science, vol 88. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14306-9_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-14306-9_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14305-2
Online ISBN: 978-3-642-14306-9
eBook Packages: Computer ScienceComputer Science (R0)