%Aigaion2 BibTeX export from Bibliography Database of the Working Group on Computational Intelligence
%Wednesday 22 January 2025 09:13:52 AM

@ARTICLE{,
    author = {Borgelt, Christian and Braune, Christian and K{\"{o}}tter, Tobias and Gr{\"{u}}n, Sonja},
     month = apr,
     title = {New Algorithms for Finding Approximate Frequent Item Sets},
   journal = {Soft Computing - A Fusion of Foundations, Methodologies and Applications},
    volume = {16},
    number = {2},
      year = {2012},
     pages = {903-917},
      issn = {1432-7643 (Print), 1433-7479 (On},
  abstract = {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.}
}