Exploring Time Series of Patterns: Guided Drill-Down in Hierarchies Using Change Mining on Frequent Item Sets

  • Mirko Böttcher
  • Martin Spott
Part of the Studies in Computational Intelligence book series (SCI, volume 445)


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 technique for a guided, visual exploration of patterns rather than presenting analysts with static ordered lists of patterns. Specifically, we focus on a method to guide drill-downs into hierarchical attributes, where we make use of change mining on frequent item sets for pattern discovery.


Association Rule Temporal Homogeneity Data Warehouse Aggregation Operator Weight History 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Faculty of Computer ScienceUniversity of MagdeburgMagdeburgGermany
  2. 2.Research and TechnologyBTIpswichUK

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