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Frequent Itemset Mining for Clustering Near Duplicate Web Documents

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Conceptual Structures: Leveraging Semantic Technologies (ICCS 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5662))

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Abstract

A vast amount of documents in the Web have duplicates, which is a challenge for developing efficient methods that would compute clusters of similar documents. In this paper we use an approach based on computing (closed) sets of attributes having large support (large extent) as clusters of similar documents. The method is tested in a series of computer experiments on large public collections of web documents and compared to other established methods and software, such as biclustering, on same datasets. Practical efficiency of different algorithms for computing frequent closed sets of attributes is compared.

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Author information

Authors and Affiliations

  1. Department of Applied Mathematics, Higher School of Economics, Kirpichnaya 33/5, Moscow, 105679, Russia

    Dmitry I. Ignatov & Sergei O. Kuznetsov

Authors
  1. Dmitry I. Ignatov
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  2. Sergei O. Kuznetsov
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Editor information

Editors and Affiliations

  1. Institut AIFB, Universität Karlsruhe (TH), 76128, Karlsruhe, Germany

    Sebastian Rudolph

  2. SAP Research Center CEC Dresden, Chemnitzer Strasse 48, 01187, Dresden, Germany

    Frithjof Dau

  3. Higher School of Economics, Department of Applied Mathematics and Information Science, State University, Kirpichnaya 33/5, 105679, Moscow, Russia

    Sergei O. Kuznetsov

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© 2009 Springer-Verlag Berlin Heidelberg

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Ignatov, D.I., Kuznetsov, S.O. (2009). Frequent Itemset Mining for Clustering Near Duplicate Web Documents. In: Rudolph, S., Dau, F., Kuznetsov, S.O. (eds) Conceptual Structures: Leveraging Semantic Technologies. ICCS 2009. Lecture Notes in Computer Science(), vol 5662. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03079-6_15

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  • DOI: https://doi.org/10.1007/978-3-642-03079-6_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03078-9

  • Online ISBN: 978-3-642-03079-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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