Learning systems in massive databases are key elements for the development of both efficient data retrieval methods and data-driven theories. Typically in fusion, waveforms have a very high dimensionality and the construction of classification systems (mainly through unsupervised techniques) without the help of visual techniques is very difficult. The Grand Tour is a 2D visual exploratory method that can be used in the clustering process. Applications to data retrieval and disruption classification in JET are presented.