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Performance Drivers for Depth-First Frequent Pattern Mining

  • Lars Schmidt-Thieme
  • Martin Schader
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Fast algorithms for mining frequent itemsets nowadays are highly optimized and specialized. Often they resemble the basic algorithms as Apriori and Eclat only faintly. But algorithms for other pattern domains as sequences etc. typically are built on top of the basic algorithms and thus cannot participate in improvements for highly specialized algorithms for itemsets.

Therefore, we would like to investigate different properties of a basic depth-first search algorithm, Eclat, and identify its performance drivers. We view Eclat as a basic algorithm and a bundle of optional algorithmic features that are taken partly from other algorithms like 1cm and Apriori, partly new ones. We evaluate the performance impact of these different features and identify the best configuration of Eclat.

Keywords

Incidence Matrix Frequent Item Mining Task Performance Driver Transaction Database 
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 2005

Authors and Affiliations

  • Lars Schmidt-Thieme
    • 1
  • Martin Schader
    • 2
  1. 1.Institute for Computer ScienceUniversity of FreiburgFreiburgGermany
  2. 2.Department of Information SystemsUniversity of MannheimMannheimGermany

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