Application of codes in modular metrics for searching k-neighbors

This paper is devoted to the application of suffix codes in the modular metric for solving clustering and k-nearest neighbors (KNN) problems. The advantages of using the modular metric over the Euclidean metric are considered, especially in high-dimensional spaces. The main emphasis is placed on the development of efficient clustering and k-nearest neighbors algorithms using codes that can correct errors in the modular metric. The proposed approach provides polynomial complexity with respect to the training sample dimension, which makes it promising for machine learning applications with large datasets and high-performance requirements.