Clustering Algorithm for Running Test Data Sequence of Computer Integrated System

This paper mainly studies the clustering algorithm classification of computer integrated system running test data sequence. For the test system that requires a faster acquisition rate, the oscilloscope has the advantages of acquisition frequency bandwidth, high sensitivity, and strong anti-interference. The data is collected through an oscilloscope with a GPIB interface, and then the OPTICS density clustering algorithm is used to test the data generated by the user. The sequence SQL operation log is clustered, and the abnormal behavior of users is extracted by analyzing each attribute in the SQL statement to form prior knowledge; Bagging, Boosting and Stacking are combined to form an integrated learning model, and related experimental data are used for testing the results show that this scheme can detect abnormal database behaviors with higher efficiency, and is better than similar schemes in terms of accuracy.