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Time Efficient and Quality Effective K Nearest Neighbor Search in High Dimension Space

Abstract 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. However, these two algorithms need a data access post-processing step after candidates' collection in order to get the final answer to the KNN query. In this thesis, Multi-Probe LSH with data access post-proce... (more)
Created Date 2011
Contributor Yu, Renwei (Author) / Candan, Kasim S (Advisor) / Sapino, Maria L (Committee member) / Chen, Yi (Committee member) / Sundaram, Hari (Committee member) / Arizona State University (Publisher)
Subject Computer Science / high dimension / K nearest neighbor / large scale / locality sensitive hash
Type Masters Thesis
Extent 81 pages
Language English
Reuse Permissions All Rights Reserved
Note M.S. Computer Science 2011
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis