User Model Clustering

DOI: 10.4236/jdaip.2014.22006   PDF   HTML   XML   4,244 Downloads   5,472 Views   Citations


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

Conflicts of Interest

The authors declare no conflicts of interest.


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[3] Neapolitan, R.E. (2003) Learning Bayesian Networks. Northeastern Illinois University Chicago, Illinois. Prentice Hall, Upper Saddle River.

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