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Refereed Papers Track

  • Data Mining
  • The web has become the primary repository of the world's knowledge. Billions of pages, maintained by individuals and organizations around the world contain content, services and semantic associations on a wide range of topics. There are also many additional (possibly implicit) corpora associated with the web such as hyperlink usage data, site transaction logs, message boards, query streams, and chat sessions. Thus the web provides a rich domain for using data mining techniques to both extract useful knowledge from this huge and diverse corpus, and to study the structural properties (size, shape, growth) and social aspects of the web (hubs, communities, weblogs). The focus of the Data Mining area of the WWW 2004 refereed papers track includes, but is not limited to, the following topics:

    • Machine learning and mining for unstructured, semistructured, and relational data.
    • Classification, clustering, collaborative recommender systems.
    • Adapting data mining techniques for web data.
    • Deriving facts, associations and lists from web data.
    • Information extraction and message understanding.
    • Mining associated corpora: queries, usage, messages, transactions.
    • Mining using hyperlink graph analysis, e.g., communities, structural properties, evolution.
    If you would like information, or to volunteer, please contact Mae Isaac, mkisaac@us.ibm.com