摘要
针对现有研究对文献主题发现和演化分析方法的单一性,本文提出了基于时间序列聚类的主题发现与演化分析方法。该方法首先通过共词分析找出文献数据集中高频关键词的共现矩阵,利用Ochiia系数计算方法将共现矩阵转换为相似性矩阵,然后使用近邻传播聚类算法发现文献主题。同时,再将主题在某段时间内的研究热度进行分析并转化为反映主题热度时间序列数据,结合时间序列聚类方法对各主题进行分类以及演化趋势的分析。实验结果表明,通过对中国知网中2000—2018年与创新管理相关的期刊文献进行数据处理与挖掘,提出的方法能有效地发现期刊的研究主题,并且能较好地分析这些主题的演化趋势。
In view of the uniqueness of the existing methods of topic discovery and evolutionary analysis in literature,this paper proposes a method of topic discovery and evolutionary analysis based on time series clustering.The co-occurrence matrix of high-frequency keywords in document datasets is found by co-word analysis.The co-occurrence matrix is transformed into a similarity matrix by the Ochiia coefficient calculation method,and then the topic of the document is found by using the nearest neighbor propagation clustering algorithm.At the same time,the research heat of each topic during a certain period is analyzed and transformed into time series data reflecting the heat of each topic,and the time series clustering method is used to classify and analyze the evolution trend of each topic.The experimental results show that the proposed method can effectively discover the research topics of journals and better analyze the evolution trends of these topics through data processing and mining of the journal literature related to innovation management in CNKI from 2000 to 2018.
作者
李海林
邬先利
Li Hailin;Wu Xianli(College of Business Administration,Huaqiao University,Quanzhou 362021)
出处
《情报学报》
CSSCI
CSCD
北大核心
2019年第10期1041-1050,共10页
Journal of the China Society for Scientific and Technical Information
基金
国家自然科学基金项目“高维时间序列数据聚类分析及应用研究”（71771094）
福建省社会科学规划项目“基于时间序列数据挖掘的期刊参考文献和引证文献分析研究”（FJ2017B065）
关键词
AP聚类
时间序列聚类
主题发现
主题演化
AP clustering
time series clustering
themes discovery
topic evolution
