Abstract: The unstructured data structure of network measurement points is not clear. In order to improve the similarity of clustering, a mathematical modeling method for clustering the similarity of unstructured data of network measurement points is studied. Using the method of unstructured data network partitioning, the unstructured data of network measurement points is transformed into semi-structured data, obtaining a semi- structured data meta path. The semi-structured data is decomposed into two non negative matrices using the non negative matrix decomposition method. The non negative matrices are multiplied and fitted, and the regularization coefficient is introduced in the process to establish a comprehensive similarity rectangle on the original path of the semi-structured data. This enables the highly similar network measurement point semi- structured data to establish a similar cluster indicator vector and construct a similarity clustering mathematical model. After the model iteration, the clustering results are more reasonable and consistent. The experimental results show that this method can effectively convert unstructured data from network measurement points into semi-structured data. The clustering density of unstructured data from network measurement points in similarity clustering is high, and the NMI(Normalized Mutual Information) value is distributed in a higher area. Its clustering performance for network measurement point unstructured data is good. 

Key words: network measurement points, unstructured data, similarity, mathematical modeling, non negative matrix factorization, similarity regularization term