ANALYSIS AND COMPARISON OF APPROXIMATE K-NEAREST NEIGHBOR ALGORITHMS
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Date
2024-04-19
Authors
Aidarbek, Angsar
Journal Title
Journal ISSN
Volume Title
Publisher
Nazarbayev University School of Engineering and Digital Sciences
Abstract
This 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.
Description
Keywords
ANN, KNN, Search, Algorithms, Information Retrieval, Type of access: Restricted
Citation
Aidarbek, Angsar. (2024) Analysis and Comparison of Approximate k-Nearest Neighbor Algorithms. Nazarbayev University School of Engineering and Digital Sciences