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K-Nearest-Neighbors (KNN) search is a fundamental problem in many application domains such as database and data mining, information retrieval, machine learning, pattern recognition and plagiarism detection. Locality sensitive hash (LSH)

K-Nearest-Neighbors (KNN) search is a fundamental problem in many application domains such as database and data mining, information retrieval, machine learning, pattern recognition and plagiarism detection. Locality sensitive hash (LSH) is so far the most practical approximate KNN search algorithm for high dimensional data. Algorithms such as Multi-Probe LSH and LSH-Forest improve upon the basic LSH algorithm by varying hash bucket size dynamically at query time, so these two algorithms can answer different KNN queries adaptively.

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    Date Created
    • 2011
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  • Text
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    • Partial requirement for: M.S., Arizona State University, 2011
      Note type
      thesis
    • Includes bibliographical references (p. 68-71)
      Note type
      bibliography
    • Field of study: Computer science

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    by Renwei Yu

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