• myGriffith
    • Staff portal
    • Contact Us⌄
      • Future student enquiries 1800 677 728
      • Current student enquiries 1800 154 055
      • International enquiries +61 7 3735 6425
      • General enquiries 07 3735 7111
      • Online enquiries
      • Staff phonebook
    View Item 
    •   Home
    • Griffith Research Online
    • Conference outputs
    • View Item
    • Home
    • Griffith Research Online
    • Conference outputs
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

  • All of Griffith Research Online
    • Communities & Collections
    • Authors
    • By Issue Date
    • Titles
  • This Collection
    • Authors
    • By Issue Date
    • Titles
  • Statistics

  • Most Popular Items
  • Statistics by Country
  • Most Popular Authors
  • Support

  • Contact us
  • FAQs
  • Admin login

  • Login
  • Fast and Robust General Purpose Clustering Algorithms

    Author(s)
    Estivill-Castro, V
    Yang, J
    Griffith University Author(s)
    Estivill-Castro, Vladimir
    Year published
    2000
    Metadata
    Show full item record
    Abstract
    General purpose and highly applicable clustering methods are required for knowledge discovery. k-Means has been adopted as the prototype of iterative model-based clustering because of its speed, simplicity and capability to work within the format of very large databases. However, k-MEANS has several disadvantages derived from its statistical simplicity. We propose algorithms that remain very efficient, generally applicable, multidimensional but are more robust to noise and outliers. We achieve this by using medians rather than means as estimators of centers of clusters. Comparison with k-Means, EM and Gibbs sampling demonstrates ...
    View more >
    General purpose and highly applicable clustering methods are required for knowledge discovery. k-Means has been adopted as the prototype of iterative model-based clustering because of its speed, simplicity and capability to work within the format of very large databases. However, k-MEANS has several disadvantages derived from its statistical simplicity. We propose algorithms that remain very efficient, generally applicable, multidimensional but are more robust to noise and outliers. We achieve this by using medians rather than means as estimators of centers of clusters. Comparison with k-Means, EM and Gibbs sampling demonstrates the advantages of our algorithms.
    View less >
    Conference Title
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume
    1886
    DOI
    https://doi.org/10.1007/3-540-44533-1_24
    Publication URI
    http://hdl.handle.net/10072/131887
    Collection
    • Conference outputs

    Footer

    Disclaimer

    • Privacy policy
    • Copyright matters
    • CRICOS Provider - 00233E

    Tagline

    • Gold Coast
    • Logan
    • Brisbane - Queensland, Australia
    First Peoples of Australia
    • Aboriginal
    • Torres Strait Islander