Mind the time: Unleashing temporal aspects in pattern discovery
We propose a new algorithm for Temporal Data Mining that combines more information in fewer patterns. The algorithm allows to freely define relations between events. A theoretical assessment of runtime and memory consumption is tested with a benchmark. We argue how the algorithm can be interactively parametrized and the results be explored by means of Visual Analytics. The algorithm and the current interface are tested in a usage scenario.
temporal data mining data mining visual analytics kdd time-oriented data pattern finding interactive visualization