@article{397,
  keywords = {Visual analytics, Time-Oriented Data, temporal data mining, Pattern Finding, KDD, Interactive  Visualization, Data Mining},
  author = {Tim Lammarsch and Wolfgang Aigner and Alessio Bertone and Silvia Miksch and Alexander Rind},
  title = {Mind the Time: Unleashing Temporal Aspects in Pattern Discovery},
  abstract = {Temporal Data Mining is a core concept of Knowledge Discovery in Databases handling time-oriented data. State-of-the-art methods are capable of preserving the temporal order of events as well as the temporal intervals in between. The temporal characteristics of the events themselves, however, can likely lead to numerous uninteresting patterns found by current approaches. We present a new definition of the temporal characteristics of events and enhance related work for pattern finding by utilizing temporal relations, like meets, starts, or during, instead of just intervals between events. These prerequisites result in a new procedure for Temporal Data Mining that preserves and mines additional time-oriented information. Our procedure is supported by an interactive visual interface for exploring the patterns. Furthermore, we illustrate the effciency of our procedure presenting an benchmark of the procedure’s run-time behavior. A usage scenario shows how the procedure can provide new insights.
},
  year = {2014},
  journal = {Computers & Graphics},
  volume = {38},
  pages = {38-50},
  url = {http://publik.tuwien.ac.at/files/PubDat_220406.pdf},
  doi = {10.1016/j.cag.2013.10.007 },
  note = {
},
}