ANALYSIS AND COMPARISON OF APPROXIMATE K-NEAREST NEIGHBOR ALGORITHMS

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dc.contributor.authorAidarbek, Angsar
dc.date.accessioned2024-05-19T13:56:42Z
dc.date.available2024-05-19T13:56:42Z
dc.date.issued2024-04-19
dc.description.abstractThis study presents a comprehensive comparison of Approximate k-Nearest Neighbor (AKNN) methods across multiple datasets, including image, text, and behavioral datasets. The performance of various AKNN algorithms is evaluated in terms of build time, search time, total time, and recall metrics for different datasets. Key findings reveal that tree-based AKNN methods exhibit vulnerability to changes in dataset contents, while graph-based algorithms demonstrate superior performance in certain scenarios. Furthermore, algorithm-specific nuances, such as computational efficiency and recall rates, are discussed across diverse datasets. Insights from this study provide valuable guidance for selecting suitable AKNN methods based on specific application requirements and dataset characteristics. Furthermore, potential directions for future research, including scalability improvements, algorithmic enhancements, and domain- specific applications are identified to further advance the field of AKNN algorithms.en_US
dc.identifier.citationAidarbek, Angsar. (2024) Analysis and Comparison of Approximate k-Nearest Neighbor Algorithms. Nazarbayev University School of Engineering and Digital Sciencesen_US
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/7684
dc.language.isoenen_US
dc.publisherNazarbayev University School of Engineering and Digital Sciencesen_US
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectANNen_US
dc.subjectKNNen_US
dc.subjectSearchen_US
dc.subjectAlgorithmsen_US
dc.subjectInformation Retrievalen_US
dc.subjectType of access: Restricteden_US
dc.titleANALYSIS AND COMPARISON OF APPROXIMATE K-NEAREST NEIGHBOR ALGORITHMSen_US
dc.typeMaster's thesisen_US
workflow.import.sourcescience

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