Outlier Preserving Clustering for Structured Data Through Kernels

In this paper, we propose a kernel-based clustering algorithm that highlights both the major trends and the atypical behaviours present in a dataset, so as to provide a complete characterisation of the data; thanks to the kernel framework, the algorithm can be applied independently of the data nature without requiring any adaptation. We apply it to xml data describing student results to several exams: we propose a kernel to handle such data and present the results obtained with a real dataset.