From Local Patterns to Classification Models

  • Björn BringmannEmail author
  • Siegfried Nijssen
  • Albrecht Zimmermann


Using pattern mining techniques for building a predictive model is currently a popular topic of research. The aim of these techniques is to obtain classifiers of better predictive performance as compared to greedily constructed models, as well as to allow the construction of predictive models for data not represented in attribute-value vectors. In this chapter we provide an overview of recent techniques we developed for integrating pattern mining and classification tasks. The range of techniques spans the entire range from approaches that select relevant patterns from a previously mined set for propositionalization of the data, over inducing patternbased rule sets, to algorithms that integrate pattern mining and model construction. We provide an overview of the algorithms which are most closely related to our approaches in order to put our techniques in a context.


Association Rule Class Label Greedy Algorithm Local Pattern Pattern Mining 
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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© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Björn Bringmann
    • 1
    Email author
  • Siegfried Nijssen
    • 1
  • Albrecht Zimmermann
    • 1
  1. 1.Department of Computer ScienceKatholieke Universiteit LeuvenLeuvenBelgium

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