Efficient Clustering of Web-Derived Data Sets

  • Luís Sarmento
  • Alexander Kehlenbeck
  • Eugénio Oliveira
  • Lyle Ungar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5632)

Abstract

Many data sets derived from the web are large, high-dimensional, sparse and have a Zipfian distribution of both classes and features. On such data sets, current scalable clustering methods such as streaming clustering suffer from fragmentation, where large classes are incorrectly divided into many smaller clusters, and computational efficiency drops significantly. We present a new clustering algorithm based on connected components that addresses these issues and so works well on web-type data.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Luís Sarmento
    • 1
  • Alexander Kehlenbeck
    • 2
  • Eugénio Oliveira
    • 1
  • Lyle Ungar
    • 3
  1. 1.Faculdade de Engenharia da Universidade do Porto - DEI - LIACCPortoPortugal
  2. 2.Google IncNew York, NYUSA
  3. 3.University of Pennsylvania - CSPhiladelphiaUSA

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