基于改进型共引聚类的技术跟踪二维可视化研究

中国管理科学 ›› 2009, Vol. 17 ›› Issue (1): 144-150.

基于改进型共引聚类的技术跟踪二维可视化研究

康宇航

杭州汽轮动力集团研究院, 浙江杭州 310016

收稿日期:2008-07-31 修回日期:2009-01-15 出版日期:2009-02-28 发布日期:2009-02-28
作者简介:康宇航(1978- ),男(汉族),辽宁大连人,杭州汽轮动力集团工作,博士,研究方向:技术情报、数据挖掘.

Technology Tracking Two-Dimensional Visualization Based on Improved Co-Citation Cluster

KANG Yu-hang

Hangzhou Turbine Group Co., LTD. Central R&D Institute, Hangzhou 310016, China

Received:2008-07-31 Revised:2009-01-15 Online:2009-02-28 Published:2009-02-28

摘要/Abstract

摘要： 通过技术跟踪,可以了解某一技术领域的现状和发展态势,为技术战略的制定提供决策信息支持.当前,基于共引聚类分析的技术跟踪研究获得了长足的发展,但仍然存在一些问题,主要表现在数据关联的计算和聚类效率的提升上.基于此,文章从构建共引关联模型,改进系统聚类算法二个方面对共引聚类进行改进,并根据改进的共引聚类方法设计了基于多维标度模型的技术跟踪二维可视化工具.在此基础上,以公路工程领域为例,从个人、机构层面进行可视化实验,并着重对中国在此领域的技术情况进行详细的分析,以此判别技术领域的深层次结构,为技术跟踪的相关研究做一些支撑性工作.

关键词: 共引聚类, 技术跟踪, 可视化

Abstract: Technology tracking is a useful method in identifying the current situation and trend of development in related technical areas, and can help us provide the information decision-making support for establishment of technical strategy.At present, technology tracking based on co-citation cluster analysis has made a rapid development but there are still some problems, mainly in two aspects:the calculation of data association and enhance of the efficiency of clustering.Based on this, the article improved co-citation cluster and a technology tracking two-dimensional visualization tools from three areas:co-citation associated model construction, improved hierarchical clustering and design multi-dimensional scale model based on dato associated.Meanwhile, author try to study visualization experiments of highway engineering from individuals and institutions in order to discriminate this field of deep-seated structural, as a kind of technical analysis which is fundamental to technology strategy selection.

Key words: co-citation cluster, technology tracking, visualization

中图分类号:

C931

康宇航. 基于改进型共引聚类的技术跟踪二维可视化研究[J]. 中国管理科学, 2009, 17(1): 144-150.

KANG Yu-hang. Technology Tracking Two-Dimensional Visualization Based on Improved Co-Citation Cluster[J]. Chinese Journal of Management Science, 2009, 17(1): 144-150.