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Constraining the Search Space in Temporal Pattern Mining

  • Agents in dynamic environments have to deal with complex situations including various temporal interrelations of actions and events. Discovering frequent patterns in such scenes can be useful in order to create prediction rules which can be used to predict future activities or situations. We present the algorithm MiTemP which learns frequent patterns based on a time intervalbased relational representation. Additionally the problem has also been transfered to a pure relational association rule mining task which can be handled by WARMR. The two approaches are compared in a number of experiments. The experiments show the advantage of avoiding the creation of impossible or redundant patterns with MiTemP. While less patterns have to be explored on average with MiTemP more frequent patterns are found at an earlier refinement level.

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Metadaten
Author:Andreas D. Lattner, Otthein Herzog
URN:https://nbn-resolving.org/urn:nbn:de:gbv:hil2-opus-443
Document Type:Conference Proceeding
Language:English
Date of Publication (online):2011/04/28
Contributing Corporation:TZI – Center for Computing Technologies, Universit¨at Bremen
Release Date:2011/04/28
Source:LWA 2006: Lernen - Wissensentdeckung - Adaptivität, Hildesheim, 9. - 11. Oktober 2006
PPN:Link zum Katalog
Contributor:Althoff, Klaus-Dieter
Institutes:Fachbereich IV / Informatik
DDC classes:000 Allgemeines, Informatik, Informationswissenschaft / 000 Allgemeines, Wissenschaft / 004 Informatik
Licence (German):License LogoDeutsches Urheberrecht