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User Model Clustering

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DOI: 10.4236/jdaip.2014.22006    3,930 Downloads   5,060 Views   Citations
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ABSTRACT

User model which is the representation of information about user is the heart of adaptive systems. It helps adaptive systems to perform adaptation tasks. There are two kinds of adaptations: 1) Individual adaptation regarding to each user; 2) Group adaptation focusing on group of users. To support group adaptation, the basic problem which needs to be solved is how to create user groups. This relates to clustering techniques so as to cluster user models because a group is considered as a cluster of similar user models. In this paper we discuss two clustering algorithms: k-means and k-medoids and also propose dissimilarity measures and similarity measures which are applied into different structures (forms) of user models like vector, overlay, and Bayesian network.

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Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Nguyen, L. (2014) User Model Clustering. Journal of Data Analysis and Information Processing, 2, 41-48. doi: 10.4236/jdaip.2014.22006.

References

[1] Han, J.W. and Kamber, M. (2006) Data Mining: Concepts and Techniques. Second Edition, Elsevier Inc., Amsterdam.
[2] Nguyen, L. and Phung, D. (2009) Combination of Bayesian Network and Overlay Model in User Modeling. Proceedings of 4th International Conference on Interactive Mobile and Computer Aided Learning (IMCL 2009), 22-24 April 2009, Amman.
[3] Neapolitan, R.E. (2003) Learning Bayesian Networks. Northeastern Illinois University Chicago, Illinois. Prentice Hall, Upper Saddle River.

  
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