Learning Pattern Graphs for Multivariate Temporal Pattern Retrieval

  • Sebastian Peter
  • Frank Höppner
  • Michael R. Berthold
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7619)

Abstract

We propose a two-phased approach to learn pattern graphs, a powerful pattern language for complex, multivariate temporal data, which is capable of reflecting more aspects of temporal patterns than earlier proposals. The first phase aims at increasing the understandability of the graph by finding common substructures, thereby helping the second phase to specialize the graph learned so far to discriminate against undesired situations. The usefulness is shown on data from the automobile industry and the libras data set by taking the accuracy and the knowledge gain of the learned graphs into account.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Sebastian Peter
    • 1
  • Frank Höppner
    • 2
  • Michael R. Berthold
    • 1
  1. 1.Nycomed-Chair for Bioinformatics and Information Mining, Dept. of Computer ScienceUniversity of KonstanzKonstanzGermany
  2. 2.Dept. of Computer ScienceOstfalia University of Applied SciencesWolfenbüttelGermany

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