New algorithms for finding approximate frequent item sets
New algorithms for finding approximate frequent item sets
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2011
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Soft Computing ; 16 (2011), 5. - pp. 903-917. - ISSN 1432-7643. - eISSN 1433-7479
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.
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004 Computer Science
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BORGELT, Christian, Christian BRAUNE, Tobias KÖTTER, Sonja GRÜN, 2011. New algorithms for finding approximate frequent item sets. In: Soft Computing. 16(5), pp. 903-917. ISSN 1432-7643. eISSN 1433-7479. Available under: doi: 10.1007/s00500-011-0776-2BibTex
@article{Borgelt2011algor-23713, year={2011}, doi={10.1007/s00500-011-0776-2}, title={New algorithms for finding approximate frequent item sets}, number={5}, volume={16}, issn={1432-7643}, journal={Soft Computing}, pages={903--917}, author={Borgelt, Christian and Braune, Christian and Kötter, Tobias and Grün, Sonja} }
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