Don’t Be Afraid of Simpler Patterns

  • Björn Bringmann
  • Albrecht Zimmermann
  • Luc De Raedt
  • Siegfried Nijssen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4213)


This paper investigates the trade-off between the expressiveness of the pattern language and the performance of the pattern miner in structured data mining. This trade-off is investigated in the context of correlated pattern mining, which is concerned with finding the k-best patterns according to a convex criterion, for the pattern languages of itemsets, multi-itemsets, sequences, trees and graphs. The criteria used in our investigation are the typical ones in data mining: computational cost and predictive accuracy and the domain is that of mining molecular graph databases. More specifically, we provide empirical answers to the following questions: how does the expressive power of the language affect the computational cost? and what is the trade-off between expressiveness of the pattern language and the predictive accuracy of the learned model? While answering the first question, we also introduce a novel stepwise approach to correlated pattern mining in which the results of mining a simpler pattern language are employed as a starting point for mining in a more complex one. This stepwise approach typically leads to significant speed-ups (up to a factor 1000) for mining graphs.


Association Rule Correlate Pattern Correlation Measure Pattern Mining Stepwise Approach 
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 2006

Authors and Affiliations

  • Björn Bringmann
    • 1
  • Albrecht Zimmermann
    • 1
  • Luc De Raedt
    • 1
  • Siegfried Nijssen
    • 1
  1. 1.Institute of Computer Science, Machine Learning LabAlbert-Ludwigs-University FreiburgFreiburgGermany

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