rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: On the effect of INQUERY term-weighting scheme on query-sensitive similarity measures
  name:
    name part: Kini, Ananth Ullal, 1979-
    role:
      role term: creator
  name:
    name part: Nelson, Paul
    role:
      role term: thesis advisor.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2006
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: remote
  abstract: Cluster-based information retrieval systems often use a similarity measure to compute the association among text documents. In this thesis, we focus on a class of similarity measures named Query-Sensitive Similarity (QSS) measures. Recent studies have shown QSS measures to positively influence the outcome of a clustering procedure. These studies have used QSS measures in conjunction with the ltc term-weighting scheme.Several term-weighting schemes have superseded the ltc term-weighing scheme and demonstrated better retrieval performance relative to the latter. We test whether introducing one of these schemes, INQUERY, will offer any benefit over the ltc scheme when used in the context of QSS measures. The testing procedure uses the Nearest Neighbor (NN) test to quantify the clustering effectiveness of QSS measures and the corresponding term-weighting scheme. The NN tests are applied on certain standard test document collections and the results are tested for statistical significance. On analyzing results of the NN test relative to those obtained for the ltc scheme, we find several instances where the INQUERY scheme improves the clustering effectiveness of QSS measures. To be able to apply the NN test,we designed a software test framework, Ferret, by complementing the features provided by dtSearch, a search engine. The test framework automates the generation of NN coefficients by processing standard test document collection data. We provide an insight into the construction and working of the Ferret test framework.
  note: "Major Subject: Computer Science"
  note: Title from author supplied metadata (automated record created on Apr. 14, 2006.)
  note: Vita.
  note: Abstract.
  note: Electronic resource] / Ananth Ullal Kini.
  note: Mode of access: World Wide Web.
  note: System requirements: World Wide Web access and Adobe Acrobat Reader.
  note: M. S. Texas A&M University 2005.
  note: Includes bibliographical references.
  note: Text (Thesis).
  subject:
    topic: Major computer science
  subject:
    topic: ltc
  subject:
    topic: Similarity Measure
  subject:
    topic: Term-Weighting
  subject:
    topic: Clustering
  subject:
    topic: Text
  subject:
    topic: Document Collection
  subject:
    topic: Nearest Neighbors
  subject:
    topic: INQUERY
  subject:
    topic: Search
  subject:
    topic: Information Retrieval
  subject:
    topic: Query-Sensitive Similarity
  identifier: http://hdl.handle.net/1969.1/3116
  record info:
    record content source: TXA
    record creation date: 060502
    record change date: 20151103064714.0
    record identifier: in00002119219