Advanced data mining methods

  • Oded Maimon
  • Mark Last
Part of the Massive Computing book series (MACO, volume 1)


Anytime algorithms (e.g., Dean and Boddy, 1988, Russell and Wefald, 1991, Zilberstein, 1996) are algorithms, which offer a trade-off between the solution quality and the computational requirements of the search process. The approach is known under a variety of names, including flexible computation, resource bounded computation, just-in-time computing, imprecise computation, design-to-time scheduling, or decision-theoretic meta-reasoning. All of these methods attempt to find the best answer possible given operational constraints. According to Zilberstein (1996), the desired properties of anytime algorithms include the following: measurable solution quality, which can be easily determined at run time; monotonicity (quality is a non-decreasing function of time); consistency of the quality w.r.t. computation time and input quality; diminishing returns of the quality over time; interruptibility of the algorithm (from here comes the term anytime); and preemptability with minimal overhead.


Mutual Information Target Attribute Conditional Entropy Performance Profile Fuzzy Measure 
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 Science+Business Media Dordrecht 2001

Authors and Affiliations

  • Oded Maimon
    • 1
  • Mark Last
    • 2
  1. 1.Tel-Aviv UniversityTel-AvivIsrael
  2. 2.University of South FloridaTampaUSA

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