## 8 Directions for Further Research  
The use of the semantic approach makes it possible to generally improve the accuracy of clustering. In this study, degrees of nodes were used to characterize nodes (words or terms) and their connections with other nodes. This is only one of the possible characteristics of a complex graph of the semantic network of a text document, which narrows the semantic model to a lexical one, in which the occurrence of words and terms is characterized only by their relative occurrence frequencies in the document. However, even this allows one to slightly increase the accuracy of clustering. The use of a semantic approach makes it possible to characterize network nodes not only by their degree, but, for example, by characteristics such as clustering coefficient and mediation coefficient. The clustering coefficient is a measure of the density of connections of a given vertex with its neighbors. It is calculated as the ratio of the actual number of links between the nearest neighbors of a node to the maximum possible number of links in which all of the nearest neighbors of this node would be connected directly to each other. The value of the clustering coefficient of a node is in the range from 0 to 1. The higher the value of the coefficient, the more significant this node is for the semantic network of the document. The mediation coefficient shows how many shortest paths between all pairs of nodes in the network pass through a given node, in relation to the total number of all of the shortest paths in the network. The value of the degree of mediation is in the range from 0 to 1. The higher the value of the degree of mediation, the more significant the role of this node in the exchange of information. The use of clustering coefficients and mediation coefficients, together with the degree of nodes, can improve the clustering of text documents due to a more accurate conveyance of meanings. In this case, the following model can be proposed:  
1. The first step is to build the semantic network of the text. 2. The second step is to determine the following characteristics for each node of the constructed network: \(a\) is the degree of the node, \(b\) is the clustering coefficient, and \(c\) is the mediation coefficient. Thus, the characteristics of each node of the graph will be described by a vector consisting of three elements (\(a\), \(b\), and \(c\)). 3. The third step is to build a node-document matrix of size \(M\) (\(M\) is the number of words or terms of the dictionary of the corpus of texts) by \(N\) (\(N\) is the number of documents in the corpus): \[L=\left| \begin{array}{cccccc}l_{1,1}&l_{1,2}&.&l_{1,i}&.&l_{1,N}\\ l_{2,1}&l_{2,2}&.&l_{2,i}&.&l_{2,N}\\.&.&.&.&.&.\\ l_{j,1}&l_{i,2}&.&l_{k,i}&.&l_{i,N}\\.&.&.&.&.&.\\ l_{M,1}&l_{M,2}&.&l_{M,i}&.&l_{M,N}\end{array} \right|\] (12) where \[l_{k,i}=\left| \begin{array}{c|c}a_{k,i}\\ b_{k,i}\\ c_{k,i}\end{array} \right|\cdot\text{\emph{id}}f_{k,i}(t_{k,i},\ D)\] (13)  
\(a_{k,i}\) is the degree of the \(k\)-th node in the \(i\)-th document; \(b_{k,i}\) is the clustering coefficient of the \(k\)-th node in the \(i\)-th document; and \(c_{k,i}\) is the mediation coefficient of the \(k\)-th node in the \(i\)-th document. The value of \(k\) varies from 1 to \(M\), and \(i\) varies from 1 to \(N\). 4 The fourth step is to build an \(N\) by \(N\) matrix of cosine metric values according to (7).