Scientific and technological paper clustering analysis method based on variational diagram auto-encoder and K-Means

• Main Authors: YANG XUHUA, LIU RUI, XU XINLI, XIAO YUNYUE, XU YINGKUN
• Format: Patent
• Language: Chinese; English
• Published: 15.12.2020
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; ELECTRIC DIGITAL DATA PROCESSING; HANDLING RECORD CARRIERS; PHYSICS; PRESENTATION OF DATA; RECOGNITION OF DATA; RECORD CARRIERS

The invention discloses a scientific and technological paper clustering analysis method based on variational diagram auto-encoder and K-Means, which comprises the following steps of: constructing a citation network G=(V, E, F) by utilizing existing scientific and technological paper data, and constructing a variational diagram auto-encoder consisting of an encoder and a decoder according to an adjacent matrix A of a citation relationship between papers and a characteristic matrix F of paper keyword attributes, taking minimization of distance measurement between a reconstructed adjacency matrixand an original adjacency matrix A and divergence of node representation vector distribution and normal distribution as targets, training in an unsupervised mode to obtain multi-dimensional Gaussiandistribution, and sampling from the distribution to obtain a low-dimensional embedded vector z of a node; and then clustering the low-dimensional embedded vector z by using the K-Means algorithm to obtain a division result of