Open AccessHierarchical feature similarity integration method for data sets based on deep learning
Author(s) -
Xiaoli Yu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1982/1/012099
Subject(s) - computer science , data mining , similarity (geometry) , feature (linguistics) , process (computing) , construct (python library) , feature vector , software , hierarchical database model , artificial intelligence , machine learning , philosophy , linguistics , image (mathematics) , programming language , operating system
Under the background of the rapid rise of open-source software and the gradual popularization of various software development tools, a large amount of development activity data has been accumulated on the Internet. In the process of using these data to construct data sets, due to their poor traceability and narrow application scope, the quality of data in development activities is not high and the accuracy of analysis results is not high. The application of the hierarchical feature similarity integration method of data sets can make the multi-version and multi-level development smoother and more orderly. In this paper, a hierarchical feature similarity integration method based on hierarchical deep learning is proposed for data sets. Firstly, the dynamic mesh partitioning method is used to divide the sparse and dense regions in the space, which reduces the scale of data detection and shortens the execution time of detection. Then, through the hierarchical deep learning process, the professional knowledge and the distribution information of data attribute value are fused to realize the detection of discrete data in the database. Experimental results show that this method can accurately complete the detection of discrete data in the database in a relatively short time, and has more application advantages than traditional methods.