Abstract
In the past years pattern detection has gained in importance for many companies. As the volume of collected data increases so does typically the number of found patterns. To cope with this problem different interestingness measures for patterns have been proposed. Unfortunately, their usefulness turns out to be limited in practical applications. To address this problem, we propose a novel visualisation technique that allows analysts to explore patterns interactively rather than presenting analysts with static ordered lists of patterns. Specifically, we focus on an interactive visualisation of temporal frequent item sets with hierarchical attributes.
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Schmidt, F., Spott, M. (2013). Visualising Temporal Item Sets: Guided Drill-Down with Hierarchical Attributes. In: Kruse, R., Berthold, M., Moewes, C., Gil, M., Grzegorzewski, P., Hryniewicz, O. (eds) Synergies of Soft Computing and Statistics for Intelligent Data Analysis. Advances in Intelligent Systems and Computing, vol 190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33042-1_57
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DOI: https://doi.org/10.1007/978-3-642-33042-1_57
Publisher Name: Springer, Berlin, Heidelberg
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