Soft Computing

, Volume 16, Issue 5, pp 903-917

First online:

New algorithms for finding approximate frequent item sets

  • Christian BorgeltAffiliated withEuropean Centre for Soft Computing Email author 
  • , Christian BrauneAffiliated withEuropean Centre for Soft ComputingDepartment of Computer Science, Otto-von-Guericke-University of Magdeburg
  • , Tobias KötterAffiliated withDepartment of Computer Science, University of Konstanz
  • , Sonja GrünAffiliated withRIKEN Brain Science InstituteInstitute of Neuroscience and Medicine (INM-6), Research Center Jülich

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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.