## 2 Review of Publications on the Subject of Research  
In [1], the issues of processing text documents that affect the accuracy of clustering are studied. In this case, dimensionality reduction methods are implemented using TF-IDF based on the frequency occurrence of words and singular value decomposition (SVD) dimensionality reduction tools. The authors test preprocessing methods and evaluate data dimensionality reduction for document clustering using the k-means algorithm. Paper [2] presents a scalable, fast, and efficient clustering algorithm based on the use of bivariate n-gram frequent elements to reduce the large data dimension by filtering them based on the frequency characteristics of the occurrence of terms in documents. The article shows that the proposed clustering algorithm works better under the condition of removing stop words from text datasets compared to without removing them. Paper [3] considers the question of the frequency of terms in news clustering. The authors believe that the TF-IDF representation of texts is biased, since the most important terms can be attributed to noise, which will lead to a decrease in the \"weight indicators\" of these terms when calculating the value of the similarity metric between text vectors. According to the authors of this work, news clustering without using the most important but rarely used terms in the text is ineffective. Paper [4] studies approaches to an improvement in the results of clustering text documents and, in particular, clustering news articles from the Internet using text n-grams during keyword extraction. The paper compares this approach with the approach previously used by the authors for clustering with the representation of vectors based on the frequencies of occurrences of only single words (i.e., without using n-grams to represent vectors). Experiments have shown that a significant improvement in clustering results can be achieved by adjusting the value of n and the weights between the n-gram keyword and its associated n-grams. In [5], a new method for clustering news documents is proposed. The authors believe that clustering based only on the distance between a pair of documents using the cosine metric is not good enough. To improve the accuracy of clustering, this study proposes a method for measuring the similarity of text vectors with refinement of the values of their elements (for example, due to various options for TF-IDF representation of texts) and subsequent clustering using the k-means algorithm. Experimental results show a significant improvement in the accuracy of clustering due to the application of the proposed method. Recently, the EM algorithm (expectation-maximization algorithm) [6; 7], which, like k-means, is iterative, has gained popularity. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These estimates are used to determine the latent variable distribution in the next step E. The EM algorithm computes the probabilities of feature membership in a cluster based on one or more probability distributions, rather than assigning features to clusters to maximize differences in mean values for continuous variables. Paper [8] considers a news clustering method based on the proximity of pieces of news' digital characteristics that was designed to simplify the processing of information in the database. The numerical characteristics may include, for example, the TF-IDF vector of the document and the sentiment of the text. News documents are placed at the vertices of the graph, and after identifying their category, they are associated with other vertices that have this category. The link weight can be set based on the value of the similarity metric of digital characteristics (for example, the cosine of the angle between text vectors).