| 基于DBScan算法的研究群体挖掘 |
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| 引用本文： | 林涵溪.基于DBScan算法的研究群体挖掘[J].计算机应用与软件,2009,26(9):110-112,125. |
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| 作者姓名： | 林涵溪 |
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| 作者单位： | 复旦大学软件学院,上海,200433 |
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| 摘 要： | 引文网络体现了文献研究内容上的相关性及知识的传递,包含了大量的研究关联性信息,被广泛地用于对文章重要性进行鉴定.但当前缺少一种在引文网络基础上识别研究群体的方法.为寻找具有相关研究的作者群体,首先研究文献之间的引用关系,建立基于引文路径的引文分析模型,最后构造相关性指标并利用DBScan算法对引文网络进行聚类.通过对文章间关联强度的定义,运用聚类方法挖掘出学术研究群体,实现了一种新颖、且复杂度较低的研究群体识别方法.
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| 关 键 词： | 引文网络 DBScan算法 聚类 |
RESEARCH COMMUNITY MINING BASED ON DBSCAN ALGORITHM |
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| Lin Hanxi.RESEARCH COMMUNITY MINING BASED ON DBSCAN ALGORITHM[J].Computer Applications and Software,2009,26(9):110-112,125. |
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| Authors: | Lin Hanxi |
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| Affiliation: | School of Software;Fudan University;Shanghai 200433;China |
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| Abstract: | Citation network reflects the connectivity in content and transmission of knowledge between scientific papers.It contains a mass of information about research relevancy,and is widely used to identify the importance of a paper.Currently there lacks a method to identify research community on the basis of citation network.In order to find the community of authors with a similar research field,the citation relationship between papers is studies and the citation analyzing model is established based on citation p... |
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| Keywords: | Citation network DBScan algorithm Clustering |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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