• DocumentCode
    2484890
  • Title

    Usage of Mined Word Associations for Text Retrieval

  • Author

    Holt, John D. ; Chung, Soon M. ; Li, Yanjun

  • Author_Institution
    Wright State Univ., Dayton
  • Volume
    2
  • fYear
    2007
  • fDate
    29-31 Oct. 2007
  • Firstpage
    45
  • Lastpage
    49
  • Abstract
    In this paper, we evaluated the efficacy of mined association rules between words for measuring the similarity between documents to enhance the text retrieval. In our experiments, for each document relevant to a query, we formed a group of documents having at least one common frequent set of words with the answer document. Then we measured the precision of the documents in the same group as an answer set to the corresponding query. This experiment was performed using a corpus of the Text retrieval conference (TREC) and search results. Our experimental results show that the frequent sets of words mined from our test database are useful in ranking query result sets to improve the precision of retrieval.
  • Keywords
    data mining; query processing; text analysis; mined word association rule; query processing; text retrieval; Artificial intelligence; Association rules; Data mining; Frequency; Indexing; Information retrieval; Itemsets; Marketing and sales; Transaction databases; USA Councils;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Tools with Artificial Intelligence, 2007. ICTAI 2007. 19th IEEE International Conference on
  • Conference_Location
    Patras
  • ISSN
    1082-3409
  • Print_ISBN
    978-0-7695-3015-4
  • Type

    conf

  • DOI
    10.1109/ICTAI.2007.171
  • Filename
    4410355