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作 者:万校基[1,2] 李海林[1] 龚燕燕 林海龙 Wan Xiaoji;Li Hailin;Gong Yanyan;Lin Hailong(College of Business Administration,Huaqiao University,Quanzhou 362021;Business Management Research Center,Huaqiao University,Quanzhou 362021)
机构地区:[1]华侨大学工商管理学院,泉州362021 [2]华侨大学东方企业管理研究中心,泉州362021
出 处:《情报学报》2022年第4期388-400,共13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“基于文献主题时间序列数据挖掘的技术预见研究”(FJ2020B088)。
摘 要:针对现有主题排序偏主观、高维数据复杂难处理等问题,提出一种基于近邻传播聚类和天际线算法的主题排序方法。结合关键词重要性和近邻传播聚类算法自适应获取文献初始核心主题,借助平均相似性系数进一步对初始主题簇进行二次近邻传播聚类。以簇中心代表关键词的篇均被引量和篇均下载量为主题热度表征指标,利用天际线算法获取主题天际线集合,通过主成分分析法实现主题排序。对中国知网2010—2020年与供应链相关的期刊文献进行数据处理和挖掘时发现,本文提出的新方法可以有效地识别供应链领域研究主题及其热度,不仅可为相关科研人员的科学选题提供指导意见,也可为相关期刊的精准选稿提供决策支持。Aiming at the objectivity of existing topics sorting and the complexity and difficulty of high-dimensional data processing, a topic ranking method based on affinity propagation clustering and skyline algorithm is proposed. On the basis of the importance of keywords and affinity propagation clustering algorithm, the initial core topics of literature are adaptively obtained. The average similarity coefficient is used to further perform secondary affinity propagation clustering for the original topic clusters. Taking the average citations and downloads of the center, typically keywords, as the index of the topic popularity, the skyline collection is achieved by employing the skyline algorithm. Finally, the topic ranking result is achieved scientifically through principal component analysis. Through data processing and mining of the supply chain literature in China National Knowledge Infrastructure(CNKI) from 2010 to 2020, it is found that the proposed new method can effectively identify the research topics and their ranking, which can not only provide the related researchers with advice and guidance in scientific topic selection, but also offer the relevant journals decision-making support of the precise selection.
分 类 号:O223[理学—运筹学与控制论] TP18[理学—数学]
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