Parallel Data Mining Revisited. Better, Not Faster

  • Zaenal Akbar
  • Violeta N. Ivanova
  • Michael R. Berthold
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7619)


In this paper we argue that parallel and/or distributed compute resources can be used differently: instead of focusing on speeding up algorithms, we propose to focus on improving accuracy. In a nutshell, the goal is to tune data mining algorithms to produce better results in the same time rather than producing similar results a lot faster. We discuss a number of generic ways of tuning data mining algorithms and elaborate on two prominent examples in more detail. A series of exemplary experiments is used to illustrate the effect such use of parallel resources can have.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Zaenal Akbar
    • 1
  • Violeta N. Ivanova
    • 1
  • Michael R. Berthold
    • 1
  1. 1.Nycomed-Chair for Bioinformatics and Information Mining, Dept. of Computer and Information ScienceUniversity of KonstanzKonstanzGermany

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