The k-nearest neighbor (KNN) algorithm has been widely used in pattern recognition, regression, outlier detection and other data mining areas. However, it suffers from the large distance computation cost, especially when dealing with big data applications. In this paper, we propose a new fast search (FS) algorithm for exact k-nearest neighbors based on optimal triangle-inequality-based (OTI) check strategy. During the procedure of searching exact k-nearest neighbors for any query, the OTI check strategy can eliminate more redundant distance computations for the instances located in the marginal area of neighboring clusters compared with the original TI check strategy. Considering the large space complexity and extra time complexity of OTI, we also propose an efficient optimal triangle-inequality-based (EOTI) check strategy. The experimental results demonstrate that our proposed two algorithms (OTI and EOTI) achieve the best performance compared with other related KNN fast search algorithms, especially in the case of dealing with high-dimensional datasets.

k最近邻（KNN）算法已广泛应用于模式识别，回归，离群值检测和其他数据挖掘领域。但是，它遭受了远距离计算成本的困扰，尤其是在处理大数据应用程序时。在本文中，我们提出了一种基于最优基于三角形不等式（OTI）的快速搜索（FS）算法，用于精确的k最近邻。在搜索任何查询的确切k最近邻居的过程中，与原始TI检查策略相比，OTI检查策略可以消除位于相邻群集边缘区域中的实例的更多冗余距离计算。考虑到OTI的大空间复杂度和额外的时间复杂度，我们还提出了一种有效的基于最佳三角不等式（EOTI）的检查策略。