Clustering and classifying are overriding techniques in machine learning. Neighborhood construction as a key step in these techniques has been extensively used for modeling local relationships between data samples, and constructing global structures from local information. The goal of the neighborhood construction process is to improve the quality of individual data point categorizing. Many applications such as detecting social network communities, bundling related edges, solving location, and routing problems all indicate the importance of this problem. This paper presents theoretical and practical studies of state-of-the-art methods in the context of neighborhood construction which is resulted in a coherent and comprehensive survey to analyze these methods. To this end, significant algorithms of neighborhood construction have been proposed to analyze data points which are very useful for the community of clustering and classifying practitioners since showing the advantages and disadvantages of each algorithm. All of them will be described and discussed deeply in different aspects, which help to select an appropriate solution for problems. A taxonomy of these algorithms is presented and their differences and some important applications are explained. Finally, the future challenges concerning the title of the present paper are outlined.

聚类和分类是机器学习中最重要的技术。邻域构建是这些技术中的关键步骤，已广泛用于对数据样本之间的局部关系进行建模，并根据局部信息构建全局结构。邻域构建过程的目标是提高单个数据点分类的质量。许多应用程序（例如，检测社交网络社区，捆绑相关边缘，解决位置和路由问题）都表明了此问题的重要性。本文介绍了邻里建设背景下的最新方法的理论和实践研究，从而对这些方法进行了连贯而全面的调查。为此，已经提出了一种重要的邻域构造算法来分析数据点，这对集群聚类和从业者非常有用，因为它们展示了每种算法的优缺点。所有这些都将在不同方面进行深入地描述和讨论，这有助于选择适当的问题解决方案。给出了这些算法的分类法，并解释了它们的区别以及一些重要的应用。最后，概述了有关本论文标题的未来挑战。这有助于为问题选择合适的解决方案。给出了这些算法的分类法，并解释了它们的区别以及一些重要的应用。最后，概述了有关本论文标题的未来挑战。这有助于为问题选择合适的解决方案。给出了这些算法的分类法，并解释了它们的区别以及一些重要的应用。最后，概述了有关本论文标题的未来挑战。