GTM: the generative topographic mapping
Neural Computation
Self-Organizing Maps
User Modeling and User-Adapted Interaction
Robust mixture modelling using the t distribution
Statistics and Computing
Simultaneous Feature Selection and Clustering Using Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Knowledge Discovery with Genetic Programming for Providing Feedback to Courseware Authors
User Modeling and User-Adapted Interaction
Robust analysis of MRS brain tumour data using t-GTM
Neurocomputing
Using genetic algorithms for data mining optimization in an educational web-based system
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Identification of fuzzy models to predict students performance in an e-learning environment
WBE'06 Proceedings of the 5th IASTED international conference on Web-based education
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Virtual campus environments are fastly becoming a mainstream alternative to traditional distance higher education. The Internet medium they use to convey content, also allows the gathering of information on students' online behaviour. The knowledge extracted from this information can be used to fit the educational proposal to the students' needs and requirements. In this study, we introduce a novel model that is capable of detecting atypical usage behavior on the cluster structure of the users of a virtual campus, while neutralizing the negative impact of outliers on the clustering process. This model can simultaneously assess the relative relevance of individual variables on the cluster structure of the users. Experiments carried out on the available data indicate that atypical students' behaviour can be identified and interpreted in terms of those variables which are best at explaining and discriminating their different typologies.