The k-nearest neighbors (k/NN) algorithm is a simple yet powerful non-parametric classifier that is robust to noisy data and easy to implement. However, with the growing literature on k/NN methods, it is increasingly challenging for new researchers and practitioners to navigate the field. This review paper aims to provide a comprehensive overview of the latest developments in the k/NN algorithm, including its strengths and weaknesses, applications, benchmarks, and available software with corresponding publications and citation analysis. The review also discusses the potential of k/NN in various data science tasks, such as anomaly detection, dimensionality reduction and missing value imputation. By offering an in-depth analysis of k/NN, this paper serves as a valuable resource for researchers and practitioners to make informed decisions and identify the best k/NN implementation for a given application.

k最近邻 ( k /NN) 算法是一种简单但功能强大的非参数分类器，对噪声数据具有鲁棒性并且易于实现。然而，随着有关k /NN 方法的文献不断增加，新的研究人员和从业者在该领域的探索变得越来越具有挑战性。本综述旨在全面概述k /NN 算法的最新发展，包括其优点和缺点、应用、基准、可用软件以及相应的出版物和引文分析。该评论还讨论了k的潜力/NN 在各种数据科学任务中的应用，例如异常检测、降维和缺失值插补。通过对k /NN进行深入分析，本文为研究人员和从业者提供了宝贵的资源，帮助他们做出明智的决策并确定给定应用的最佳k /NN 实现。