Mining fault-tolerant item sets using subset size occurrence distributions

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GAMA, João, ed., Elizabeth BRADLEY, ed., Jaakko HOLLMÉN, ed.. Advances in Intelligent Data Analysis X. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp. 43-54. Lecture Notes in Computer Science. 7014. ISBN 978-3-642-24799-6. Available under: doi: 10.1007/978-3-642-24800-9_7
Zusammenfassung

Mining fault-tolerant (or approximate or fuzzy) item sets means to allow for errors in the underlying transaction data in the sense that actually present items may not be recorded due to noise or measurement errors. In order to cope with such missing items, transactions that do not contain all items of a given set are still allowed to support it. However, either the number of missing items must be limited, or the transaction's contribution to the item set's support is reduced in proportion to the number of missing items, or both. In this paper we present an algorithm that efficiently computes the subset size occurrence distribution of item sets, evaluates this distribution to find fault-tolerant item sets, and exploits intermediate data to remove pseudo (or spurious) item sets. We demonstrate the usefulness of our algorithm by applying it to a concept detection task on the 2008/2009 Wikipedia Selection for schools.

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004 Informatik
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Data Mining, Frequent Pattern Mining
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ISO 690BORGELT, Christian, Tobias KÖTTER, 2011. Mining fault-tolerant item sets using subset size occurrence distributions. In: GAMA, João, ed., Elizabeth BRADLEY, ed., Jaakko HOLLMÉN, ed.. Advances in Intelligent Data Analysis X. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp. 43-54. Lecture Notes in Computer Science. 7014. ISBN 978-3-642-24799-6. Available under: doi: 10.1007/978-3-642-24800-9_7
BibTex
@inproceedings{Borgelt2011Minin-15342,
  year={2011},
  doi={10.1007/978-3-642-24800-9_7},
  title={Mining fault-tolerant item sets using subset size occurrence distributions},
  number={7014},
  isbn={978-3-642-24799-6},
  publisher={Springer Berlin Heidelberg},
  address={Berlin, Heidelberg},
  series={Lecture Notes in Computer Science},
  booktitle={Advances in Intelligent Data Analysis X},
  pages={43--54},
  editor={Gama, João and Bradley, Elizabeth and Hollmén, Jaakko},
  author={Borgelt, Christian and Kötter, Tobias}
}
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