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Frequent Itemset Mining for Clustering Near Duplicate Web Documents

  • Dmitry I. Ignatov
  • Sergei O. Kuznetsov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5662)

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.

Keywords

Association Rule Formal Concept Minimal Element Document Image Formal Concept Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Dmitry I. Ignatov
    • 1
  • Sergei O. Kuznetsov
    • 1
  1. 1.Department of Applied MathematicsHigher School of EconomicsMoscowRussia

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