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Understanding I/O for data-intense applications is the foundation for the optimization of these applications. The classification of the applications according to the expressed I/O access pattern eases the analysis. An access pattern can be seen as fingerprint of an application. In this paper, we address the classification of traces. Firstly, we convert them first into a weighted string representation. Due to the fact that string objects can be easily compared using kernel methods, we explore their use for fingerprinting I/O patterns. To improve accuracy, we propose a novel string kernel function called kast2 spectrum kernel. The similarity matrices, obtained after applying the mentioned kernel over a set of examples from a real application, were analyzed using kernel principal component analysis and hierarchical clustering. The evaluation showed that two out of four I/O access pattern groups were completely identified, while the other two groups conformed a single cluster due to the intrinsic similarity of their members. The proposed strategy can be promisingly applied to other similarity problems involving tree-like structured data.