The Problem of Analyzing Data Models in Non-Euclidean Spaces in Artificial Intelligence Systems

This article provides an overview of the problems of analyzing data models in non-Euclidean spaces in artificial intelligence systems. Both clustering and classification of information as methods of data analysis are widely used in AI systems. When working with Euclidean space, we do not always get the result that is correct for us in one context or another, so we use non-Euclidean spaces, the result of which satisfies us more. When working with these spaces, we face various problems, one of which is the lack of clear rules for measuring the distance, finding the length between points, selecting the right coordinates so that it is easier to "straighten" the segment and subsequently work with the analyzed information.