Soft Computing

, Volume 16, Issue 5, pp 903–917 | Cite as

New algorithms for finding approximate frequent item sets

  • Christian BorgeltEmail author
  • Christian Braune
  • Tobias Kötter
  • Sonja Grün


In standard frequent item set mining a transaction supports an item set only if all items in the set are present. However, in many cases this is too strict a requirement that can render it impossible to find certain relevant groups of items. By relaxing the support definition, allowing for some items of a given set to be missing from a transaction, this drawback can be amended. The resulting item sets have been called approximate, fault-tolerant or fuzzy item sets. In this paper we present two new algorithms to find such item sets: the first is an extension of item set mining based on cover similarities and computes and evaluates the subset size occurrence distribution with a scheme that is related to the Eclat algorithm. The second employs a clustering-like approach, in which the distances are derived from the item covers with distance measures for sets or binary vectors and which is initialized with a one-dimensional Sammon projection of the distance matrix. We demonstrate the benefits of our algorithms by applying them to a concept detection task on the 2008/2009 Wikipedia Selection for schools and to the neurobiological task of detecting neuron ensembles in (simulated) parallel spike trains.


Spike Train Copy Probability Item Cover Supporting Transaction Fiedler Vector 
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.



This work was partially supported by the European Commission under the 7th Framework Program FP7-ICT-2007-C FET-Open, contract no. BISON-211898, and by the Helmholtz Alliance on Systems Biology.


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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Christian Borgelt
    • 1
    Email author
  • Christian Braune
    • 1
    • 2
  • Tobias Kötter
    • 3
  • Sonja Grün
    • 4
    • 5
  1. 1.European Centre for Soft ComputingMieres (Asturias)Spain
  2. 2.Department of Computer ScienceOtto-von-Guericke-University of MagdeburgMagdeburgGermany
  3. 3.Department of Computer ScienceUniversity of KonstanzConstanceGermany
  4. 4.RIKEN Brain Science InstituteWako-ShiJapan
  5. 5.Institute of Neuroscience and Medicine (INM-6)Research Center JülichJülichGermany

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