基于Web of Science的高维数据异常检测研究文献计量分析
Bibliometric analysis of high-dimensional data anomaly detection based on Web of Science
常鑫 1田立勤 1崔宸 1任广东1
作者信息
- 1. 华北科技学院计算机学院,廊坊 065201
- 折叠
摘要
针对数据维度与规模的不断增加,高维空间下异常检测愈发困难的问题,以Web of Science核心合集数据库为样本数据,通过可视化软件CiteSpace生成可视化图谱进行文献计量.结果表明:高维数据异常检测领域自1999年起,整体呈稳定上升趋势,中国发文数量最多但成果质量不如英国和日本.随着机器学习和深度学习等新兴智能技术的革新发展,将优化后的算法技术应用到具体场景,将会是该领域的未来发展趋势.
Abstract
To address the problem of increasing data dimensions and scale and the increasing difficulty of anomaly detection in high-dimensional space,the web of science core ensemble database is used as the sample data,and the visualization software CiteSpace is used to generate visual maps for bibliometrics.The results show that the field of high-dimensional data anomaly detec-tion has shown a steady upward trend since 1999,and China has the largest number of publications but the quality of the results is not as good as that of England and Japan.With the innovation and development of emerging intelligent technologies such as ma-chine learning and deep learning,the application of optimized algorithmic techniques to specific scenarios will be the future devel-opment trend of this field.
关键词
高维数据/异常检测/离群检测/文献计量法/CiteSpaceKey words
high-dimensional data/anomaly detection/outlier detection/literature measurement/CiteSpace引用本文复制引用
基金项目
中央高校基本科研业务费专项(3142023063)
出版年
2024
NETL
NSTL
万方数据