计算机科学
聚类分析
非负矩阵分解
文字2vec
背景(考古学)
主题模型
人工智能
数据挖掘
机器学习
模式识别(心理学)
矩阵分解
古生物学
特征向量
物理
嵌入
量子力学
生物
作者
Wenbo Cui,Jinling Li,Tao Zhang,Sibo Zhang
标识
DOI:10.5771/0943-7444-2023-4-257
摘要
In this study, we propose a recognition method of measuring literature topic evolution paths based on K-means-NMF in order to address problems such as the unobvious effect of topic clustering, high degree of mixing in clustering results, and unclear topic evolution paths that exist in the current research of topic evolution analysis. Firstly, we enhance the traditional NMF (Nonnegative Matrix Factorization) topic model by combining the K-means clustering algorithm with the NMF model to improve the accuracy of topic clustering and reduce the correlation among topics. Secondly, we perform the topic co-occurrence analysis based on the clustering results to identify important topic categories for recognizing critical evolution paths to solve the problem of multiple possible evolution paths in the experiment. Thirdly, we adopt the Word2Vec model to calculate topic word vectors in a semantic context to improve the accuracy of the correlation strength between topics at adjacent stages. Finally, we adopt the above method to conduct an empirical study using intelligent algorithms as an example. The experimental results show that this research method effectively identifies important topics and topic developments in the subject area, which can support scientific research and science and technology policy development.
科研通智能强力驱动
Strongly Powered by AbleSci AI