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Survey on Exact kNN Queries over High-Dimensional Data Space.

Nimish Ukey1, Zhengyi Yang1, Binghao Li2

  • 1School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia.

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|January 21, 2023
PubMed
Summary

This survey provides a comprehensive overview of exact k-nearest neighbors (kNN) queries, covering 20 kNN Search and 9 kNN Join methods for high-dimensional data. It categorizes algorithms by indexing, partitioning, clustering, and computing paradigms, offering insights into their evolution and future directions.

Keywords:
high-dimensional datakNN JoinkNN SearchkNN queries

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Area of Science:

  • Computer Science
  • Data Mining
  • Database Systems

Background:

  • k-nearest neighbors (kNN) queries are crucial for diverse applications like data mining, recommendation systems, and Industry 4.0.
  • Existing research offers numerous algorithms for kNN queries on static and dynamic data, but a comprehensive survey of exact methods, especially for high-dimensional spaces, is lacking.

Purpose of the Study:

  • To present a comprehensive survey of exact k-nearest neighbors (kNN) queries, focusing on high-dimensional data.
  • To systematically review and categorize existing exact kNN Search and kNN Join algorithms.

Main Methods:

  • The study surveys 20 kNN Search methods and 9 kNN Join methods for exact kNN queries.
  • Algorithms are categorized based on indexing strategies, data and space partitioning, clustering techniques, and computing paradigms.

Main Results:

  • This work is the first comprehensive survey of exact kNN queries specifically over high-dimensional datasets.
  • The categorization provides insights into the evolution and interrelationships of various kNN algorithms.

Conclusions:

  • The survey offers a structured overview of exact kNN query approaches in high-dimensional spaces.
  • Identifies open challenges and suggests future research directions for kNN query optimization and development.