## 7 Findings and Conclusions  
The UMAP algorithm that implements a non-linear dimensionality reduction in the original dataset through embedding it in a space of a lower dimension, and provides the possibility of its 2D visualization, shows qualitatively that the best option for dividing documents into clusters is to create their TF-IDF vectors and a matrix of cosine metric values based on the model of their lexico-semantic bond. However, even in this case, the choice of the TF-IDF model for the subsequent calculation of the cosine metric between vectors can have a significant impact on the accuracy and completeness of the further clustering of text documents. The best result can be obtained for the model of the lexico-semantic bond if, in order to calculate the lexical matrix of cosine metric values, we use document vectors based on the occurrence frequency of objects in documents (2) and the probabilistic inverse document frequency (6) using the term occurrence frequency in documents, as well as a semantic matrix of cosine metric values which is calculated based on the vector presentation using the double normalized occurrence frequency of an object in the document (3) and the inverse document frequency (4) for node/term degrees in document semantic networks. For such an analysis of the joint influence of lexical and semantic characteristics, we can move from the lexical and semantic matrices of the cosine metric values between vectors to the matrix of their averaged values. When constructing such a matrix, we took the angle cosine between vectors calculated using the document lexical characteristics from one matrix and the angle cosine between the same vectors calculated using the semantic characteristics from the other matrix. Then, we averaged them and recorded the resulting value into a new matrix. In this case, we had a square matrix that was symmetric with respect to the main diagonal and had the same size as the original matrices. The averaged values took into account both the lexical and semantic properties of the documents. The use of these values improved the clustering quality and provided a clearer division into distinct clusters. We saw the best clustering result when using root mean square averaging. The approach that was described above allowed us to develop new clustering algorithms based on the lexical and semantic properties of documents and the statistical distribution characteristics of similarity matrices for their vectors. Following these studies, we can draw the following conclusions:  
1. The clustering accuracy of text documents using the affinity propagation algorithm is 29%, 31%, and 35%, respectively, for the lexical, semantic, and combined approaches. The transition from the lexical representation of documents to the semantic one (Table 3) makes it possible to slightly increase (by approximately 3% absolute or 10% relative) the clustering accuracy and reduce the number of clusters. The transition from the semantic approach to the combined one (Table 3) leads to a slight increase in the number of clusters and an increase in the clustering accuracy (by about 3% absolute or about 10% relative). The clustering completeness in the lexical, semantic, and combined approaches is 15%, 23%, and 18%, respectively. 2. The clustering accuracy of text documents when using the algorithm based on their lexico-semantic relationship and statistical properties of distributions of similarity metrics using the procedure for combining clusters containing a small number of texts is 31%, 31%, and 35%, respectively, for the lexical, semantic, and combined approaches. The clustering completeness in the lexical, semantic, and combined approaches is 12%, 12%, and 13%. The accuracy of clustering without the use of the procedure for combining clusters is 34%, 34%, and 37%, respectively, and the recall is 11%, 11%, and 11%, respectively.