Our research on data mining focuses on the development of statistical and machine learning methods that are able to reveal the temporal structure in the data.
We are particularly interested in applications which involve massive sequential datasets collected at high frequency. Members of the Centre have focused their research in credit scoring, for classification of credit applications in changing dynamic environments and churn predictive modelling (Pavlidis et al., 2012, Crone and Finlay, 2012). Moreover, issues arising in data pre-processing and model evaluation have been extensively investigated.
A full list of publications can be found here.