k-means

The article addresses the problem of text classification in the context of growing information flows and the need for automated content analysis. A universal information technology is proposed, combining classical machine learning methods with the potential of Large Language Models for processing news, scientific, literary, journalistic and legal texts. Using the BBC News corpus (2225 texts), k-means clustering with TF-IDF demonstrated clear thematic grouping.

The decomposition of the problem is based on clustering the input set of points using the well-known k- means method, combined with an algorithm for extending partial solutions within clusters. k-means clustering algorithm is examined for partitioning the input data set of large-scale TSP instances into smaller subproblems. The efficiency of using it to reduce problem size is substantiated. Based on experiments, the application of a hierarchical version of the algorithm is proposed for problems with more than one million points.

This research presents the development and implementation of information technology for monitoring and analyzing segments of a mobile operator's stores using clustering methods. The study addresses a pertinent issue in marketing and business optimization, namely the enhancement of strategies for the network of mobile communication stores.

As a result of the research, one of the approaches to building an intelligent information system based on the recommendation of products to users with a solution to the cold start problem is described and modeled. The conducted research takes into account the advantages and disadvantages of the meth- ods, as well as their compatibility, when combining them, which is an important factor for the speed of the system and the efficiency of the algorithm.