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September 10, 2025Scientific ReportsOpen Access

Quantum granular-ball generation methods and their application in KNN classification

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Authors

SYSuzhen YuanXTXiaohua TianWLWenping Lin

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Overview

This paper demonstrates improved time efficiency in granular-ball generation methods, suggesting benefits for k-nearest neighbors classification.

Key Points

  • The proposed iterative splitting method significantly reduces time complexity in generating granular-balls, enhancing algorithm efficiency.
  • The fixed-split granular-ball generation method provides quadratic acceleration over the iterative technique, marking a substantial improvement.
  • A new quantum k-nearest neighbors algorithm is introduced, demonstrating effectiveness in handling data classification tasks.
  • Improving granular-ball generation methods could lead to broader applications in data processing techniques.

Cite This Study

Yuan et al. (2025) studied this question.

synapsesocial.com/papers/68c1c23554b1d3bfb60ef9cehttps://doi.org/10.1038/s41598-025-14724-3
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  4. 4MGNR: A Multi-Granularity Neighbor Relationship and Its Application in KNN Classification and Clustering Methods2024 · 57 citations
  5. 5Generation of Granular-Balls for Clustering Based on the Principle of Justifiable Granularity2024