Please wait a minute...
Advanced Search
数据分析与知识发现  2022, Vol. 6 Issue (1): 91-100     https://doi.org/10.11925/infotech.2096-3467.2021.0858
     研究论文 本期目录 | 过刊浏览 | 高级检索 |
融合聚类信息的技术主题图可视化方法研究*
汪雪锋1(),任惠超1,刘玉琴2
1北京理工大学管理与经济学院 北京 100081
2北京印刷学院新闻出版学院 北京 102600
Visualization Method for Technology Theme Map with Clustering
Wang Xuefeng1(),Ren Huichao1,Liu Yuqin2
1School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China
2School of Journalism and Publishing, Beijing Institute of Graphic Communication, Beijing 102600, China
全文: PDF (4277 KB)   HTML ( 35
输出: BibTeX | EndNote (RIS)      
摘要 

【目的】 弥补主题聚类后单一色彩技术主题图可视化辨识度有限,增强聚类后技术主题图的表现力,丰富科技情报分析人员的技术主题图可视化方法和软件工具选择范围。【方法】 提出融合聚类信息的技术主题图可视化方法,应用网络布局算法进行主题词的平面布局,建立平面像素点类密度函数、色彩强度函数,依据类密度和色彩强度值进行色彩渲染,得到聚类后的技术主题图。【结果】 该可视化方法嵌入到文本挖掘与可视化软件工具ItgInsight中,并应用于量子密码通信专利数据进行案例分析,结果表明该方法简单有效。【局限】 绘制的技术主题图是非矢量图,绘制效率可进一步优化。【结论】 融合聚类信息的技术主题图可视化方法增强了主题区分度,可更好地揭示技术主题结构、技术主题之间的关系。

服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
汪雪锋
任惠超
刘玉琴
关键词 技术分布主题图可视化聚类    
Abstract

[Objective] This paper tries to improve the mono-color technology topic maps generated with the clustering technique, aiming to enrich the visualization tools. [Methods] We proposed a new model to create technology topic maps with clustering. It used the layout algorithm to collect the topic words, and established the functions for pixel density, class density, as well as color intensity. We also conducted the color rendering based on the class density and color intensity, and obtained the technology topic maps. [Results] We embedded the new algorithm with ItgInsight,a text mining and visualization tool, and examined it with quantum cryptography communication patent data. The proposed method is simple and effective. [Limitations] The generated subject map is not a vector one, and the algorithm's efficiency can be further optimized. [Conclusions] The proposed method integrates clustering information and enhances topic discrimination, which help us create better technology topic maps.

Key wordsTechnical Distribution    Theme Map    Visualization    Clustering
收稿日期: 2021-08-19      出版日期: 2022-02-22
ZTFLH:  TP391  
基金资助:*本文系国家自然科学基金项目的研究成果之一(72074020)
通讯作者: 汪雪锋,ORCID:0000-0002-4857-6944     E-mail: wxf122@bit.edu.cn
引用本文:   
汪雪锋, 任惠超, 刘玉琴. 融合聚类信息的技术主题图可视化方法研究*[J]. 数据分析与知识发现, 2022, 6(1): 91-100.
Wang Xuefeng, Ren Huichao, Liu Yuqin. Visualization Method for Technology Theme Map with Clustering. Data Analysis and Knowledge Discovery, 2022, 6(1): 91-100.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2021.0858      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2022/V6/I1/91
Fig.1  屏幕格子划分与密度函数计算范围示意
Fig.2  热力图形式技术主题图
Fig.3  密度图形式技术主题图
Fig.4  多色地形图形式技术主题图
Fig.5  单色地形图形式技术主题图
Fig.6  融合聚类信息的技术主题图(普通色彩强度函数)
Fig.7  融合聚类信息的技术主题图(幂函数色彩强度函数)
Fig.8  聚类形式的量子密码通信技术主题图
Fig.9  热力图形式的量子密码通信技术主题图
Fig.10  多色地形图形式量子密码通信技术主题图
[1] Wise J A, Thomas J J, Pennock K, et al. Visualizing the Non-Visual: Spatial Analysis and Interaction with Information from Text Documents[EB/OL].[2021-05-17]http://www.cs.duke.edu/courses/spring03/cps296.8/papers/vis_non_visual.pdf .
[2] Davidson G S, Hendrickson B, Johnson D K, et al. Knowledge Mining with VxInsight: Discovery Through Interaction[J]. Journal of Intelligent Information Systems, 1998, 11(3):259-285.
doi: 10.1023/A:1008690008856
[3] Honkela T, Kaski S, Kohonen T, et al. Self-organizing Maps of Very Large Document Collections: Justification for the WEBSOM Method[C]// Proceedings of the 21st Annual Conference of the Gesellschaft für Klassifikation, 1998: 245-252.
[4] 刘玉琴, 逄金辉, 崔志成, 等. 一种简易的技术主题图绘制方法[J]. 图书情报工作, 2017, 61(13):125-132.
[4] ( Liu Yuqin, Pang Jinhui, Cui Zhicheng, et al. An Economic Method of Drawing a Technology Theme Map[J]. Library and Information Service, 2017, 61(13):125-132.)
[5] 陈挺, 王海名, 王小梅. 基于可视化的基金资助热点及其演化发现方法研究[J]. 数据分析与知识发现, 2020, 4(2-3):60-67.
[5] ( Chen Ting, Wang Haiming, Wang Xiaomei. Detecting Funding Topics Evolutions with Visualization[J]. Data Analysis and Knowledge Discovery, 2020, 4(2-3):60-67.)
[6] VOSviewer[EB/OL].[2021-05-17] http://www.vosviewer.com .
[7] True Teller[EB/OL]. [2021-05-17]http://www.trueteller.net .
[8] Beck D F, Boyack K W, Bray O H, et al. Landscapes, Games, and Maps for Technology Planning[J]. Chemtech, 1999, 29(6):8-16.
[9] VxInsight Tutorial[EB/OL].[2016-05-09]. http://iv.slis.indiana.edu/lm/lm-vx-insight.html .
[10] Innovation[EB/OL]. [2021-05-17].https://www.innovation.com .
[11] New Information Mapping Tech Now Available from Cartia[EB/OL].[2021-05-17].https://www.hpcwire.com/1998/10/23/new-information-mapping-tech-now-available-cartia/ .
[12] IncoPat[EB/OL]. [2021-05-17].https://www.incopat.com .
[13] 王蒙, 许鑫. 主题图技术在非物质文化遗产信息资源组织中的应用研究——以京剧、昆曲为例[J]. 图书情报工作, 2015, 59(14):15-21.
[13] ( Wang Meng, Xu Xin. Research on the Application of Topic Maps in Intangible Cultural Heritage Information Resource Organization: Taking Beijing Opera and Kunqu Opera as Examples[J]. Library and Information Service, 2015, 59(14):15-21.)
[14] 毛彦妮. 基于主题图的电子商务领域知识库构建研究[J]. 情报科学, 2014, 32(12):119-122.
[14] ( Mao Yanni. Construction of Domain Repository Based on Topic Maps for E-Commerce[J]. Information Science, 2014, 32(12):119-122.)
[15] 熊回香, 邓敏, 郭思源. 标签主题图的构建与实现研究[J]. 图书情报工作, 2014, 58(7):107-112.
[15] ( Xiong Huixiang, Deng Min, Guo Siyuan. Research on the Construction and Implementation of Tag Topic Maps[J]. Library and Information Service, 2014, 58(7):107-112.)
[16] 李英英, 王惠临. 主题图技术在消费者健康信息资源组织中的应用——以糖尿病为例[J]. 现代图书情报技术, 2013(12):55-61.
[16] ( Li Yingying, Wang Huilin. Application of Topic Maps in Consumer Health Information Resources Organization——Illustrated by Diabetes Mellitus Information Resources[J]. New Technology of Library and Information Service, 2013(12):55-61.)
[17] 胡娟, 程秀峰, 叶光辉. 基于主题图的学术博客知识组织模型研究[J]. 图书情报工作, 2012, 56(24):127-132.
[17] ( Hu Juan, Cheng Xiufeng, Ye Guanghui. Knowledge Organization Model of Academic Blog Based on Topic Map[J]. Library and Information Service, 2012, 56(24):127-132.)
[18] 李清茂. 基于主题图的旅游文献组织方法研究[J]. 现代图书情报技术, 2009(4):82-87.
[18] ( Li Qingmao. Research on Topic Maps Based Tourism Document Organization Method[J]. New Technology of Library and Information Service, 2009(4):82-87.)
[19] 施韶亭, 曹方. 文本挖掘技术在科技管理领域热点主题抽取方向的应用研究[J]. 计算机应用与软件, 2012, 29(7):109-111, 140.
[19] ( Shi Shaoting, Cao Fang. Applied Study on Text Mining Technique to S&T Management Field Hot Topic Extraction[J]. Computer Applications and Software, 2012, 29(7):109-111, 140.)
[20] 汪雪锋, 张硕, 刘玉琴, 等. 中国科技评价研究40年: 历史演进及主题演化[J]. 科学学与科学技术管理, 2018, 39(12):67-80.
[20] ( Wang Xuefeng, Zhang Shuo, Liu Yuqin, et al. Forty Years of Research on Science and Technology Evaluation in China: Historical and Theme Evolution[J]. Science of Science and Management of S.& T., 2018, 39(12):67-80.)
[21] 刘俊晓, 孟祥增, 齐燕, 等. 基于WOS数据的教育技术学学科交叉研究[J]. 现代远距离教育, 2019(2):14-24.
[21] ( Liu Junxiao, Meng Xiangzeng, Qi Yan, et al. Studies on Interdisciplinarity of Educational Technology Based on Web of Science-covered Data[J]. Modern Distance Education, 2019(2):14-24.)
[22] Huang Y, Zhu D, Qian Y, et al. A Hybrid Method to Trace Technology Evolution Pathways: A Case Study of 3D Printing[J]. Scientometrics, 2017, 111(1):185-204.
doi: 10.1007/s11192-017-2271-8
[23] Li R, Wang X, Liu Y, et al. Research Status and Collaboration Analysis Based on Big Data Mining: An Empirical Study of Alzheimer's Disease[J]. Technology Analysis & Strategic Management, 2021, 33(4):379-395.
[24] 樊璐璐, 吴进军, 邱城, 等. 基于专利分析的先进铸造前沿热点技术研究[J]. 铸造, 2020, 69(12):1277-1283.
[24] ( Fan Lulu, Wu Jinjun, Qiu Cheng, et al. Research Status and Developing Hotspot of Advanced Foundry Technology Based on Patent Analysis[J]. Foundry, 2020, 69(12):1277-1283.)
[25] 龚惠群, 黄超. 基于文献计量和专利分析的云计算产业竞争态势研究[J]. 中国科技论坛, 2020(10):17-27.
[25] ( Gong Huiqun, Huang Chao. Research on the Competitive Situation of Cloud Computing Industry Based on Bibliometric and Patent Analysis[J]. Forum on Science and Technology in China, 2020(10):17-27.)
[26] 李国秋, 范晓婷. 新能源汽车全球专利分析[J]. 现代情报, 2017, 37(7):123-130.
[26] ( Li Guoqiu, Fan Xiaoting. Global Patent Analysis of New Energy Vehicle[J]. Journal of Modern Information, 2017, 37(7):123-130.)
[27] 李文娟, 刘桂锋, 卢章平. 基于专利分析的我国大数据产业技术竞争态势研究[J]. 情报杂志, 2015, 34(7):65-70.
[27] ( Li Wenjuan, Liu Guifeng, Lu Zhangping. A Study on the Competition Situation of Big Data Technology in China on the Basis of Patent Analysis[J]. Journal of Intelligence, 2015, 34(7):65-70.)
[28] 丁礼谦, 张建辉, 杨萌, 等. 面向智能船舶相关技术的专利分析研究[J]. 科技管理研究, 2020(3):107-114.
[28] ( Ding Liqian, Zhang Jianhui, Yang Meng, et al. Patent Analysis of Related Technologies for Intelligent Ships[J]. Science and Technology Management Research, 2020(3):107-114.)
[29] 刘桂锋, 王秀红. Aureka专利分析工具的文献计量分析[J]. 现代情报, 2011, 31(7):106-110.
[29] ( Liu Guifeng, Wang Xiuhong. A Bibliometric Analysis of Aureka Patent Analysis Tool[J]. Journal of Modern Information, 2011, 31(7):106-110.)
[30] 肖沪卫. 用Aureka软件制作专利地图[J]. 竞争情报, 2010(3):51-58.
[30] ( Xiao Huwei. Make Patent Map with Aureka Software[J]. Competitive Intelligence, 2010(3):51-58.)
[31] Frantzi K, Ananiadou S, Mima H. Automatic Recognition of Multi-Word Terms: The C-value/NC-value Method[J]. International Journal on Digital Libraries, 2000, 3(2):115-130.
doi: 10.1007/s007999900023
[32] Eades P. A Heuristic for Graph Drawing[J]. Congressus Nutnerantiunt, 1984, 42:194-202.
[33] Kamada T, Kawai S. An Algorithm for Drawing General Undirected Graphs[J]. Information Processing Letters, 1989, 31:7-15.
doi: 10.1016/0020-0190(89)90102-6
[34] Fruchterman T M J, Reingold E M. Graph Drawing by Force Directed Placement[J]. Software Practice and Experience, 1991, 21(11):1129-1164.
doi: 10.1002/(ISSN)1097-024X
[35] RGB和XYZ色彩空间的相互转换矩阵[EB/OL]. [2021-05-17].https://blog.csdn.net/vily_lei/article/details/85679168 .
[35] (RGB/XYZ Color Space Convert[EB/OL]. [2021-05-17].].https://blog.csdn.net/vily_lei/article/details/85679168 .)
[36] 在视觉感知线性变化的色彩空间中进行颜色插值[EB/OL].[2021-05-17].https://blog.csdn.net/kun1234567/article/details/7790856 .
[36] (Color Interpolation in the Color Space of Linear Visual Perception[EB/OL].[2021-05-17].https://blog.csdn.net/kun1234567/article/details/7790856 .)
[37] ColorUtils[EB/OL].[2021-05-17].https://git.blackmarble.sh/0dayallday/iot/guardzilla/blob/db8e5b68f27fd1723e3f1d5acab7e171238c1820/Android/com.practecol.guardzilla2_source_from_JADX/sources/android/support/v4/graphics/ColorUtils.java .
[38] 刘玉琴, 汪雪锋, 雷孝平. 科研关系构建与可视化系统设计与实现[J]. 图书情报工作, 2015, 59(8):103-110, 125.
[38] ( Liu Yuqin, Wang Xuefeng, Lei Xiaoping. Design and Implementation of Academic Relation and Visualization System[J]. Library and Information Service, 2015, 59(8):103-110,125.)
[1] 王若琳, 牛振东, 蔺奇卡, 朱一凡, 邱萍, 陆浩, 刘东磊. 基于异质信息嵌入与RNN聚类参数预测的作者姓名消歧方法*[J]. 数据分析与知识发现, 2021, 5(8): 13-24.
[2] 王晰巍,贾若男,韦雅楠,张柳. 多维度社交网络舆情用户群体聚类分析方法研究*[J]. 数据分析与知识发现, 2021, 5(6): 25-35.
[3] 卢利农,祝忠明,张旺强,王小春. 基于Lingo3G聚类算法的机构知识库跨库知识整合与知识指纹服务实现[J]. 数据分析与知识发现, 2021, 5(5): 127-132.
[4] 张梦瑶, 朱广丽, 张顺香, 张标. 基于情感分析的微博热点话题用户群体划分模型 *[J]. 数据分析与知识发现, 2021, 5(2): 43-49.
[5] 丁浩, 艾文华, 胡广伟, 李树青, 索炜. 融合用户兴趣波动时序的个性化推荐模型*[J]. 数据分析与知识发现, 2021, 5(11): 45-58.
[6] 杨辰, 陈晓虹, 王楚涵, 刘婷婷. 基于用户细粒度属性偏好聚类的推荐策略*[J]. 数据分析与知识发现, 2021, 5(10): 94-102.
[7] 于丰畅,程齐凯,陆伟. 基于几何对象聚类的学术文献图表定位研究[J]. 数据分析与知识发现, 2021, 5(1): 140-149.
[8] 邬金鸣,侯跃芳,崔雷. 基于医学主题词标引规则的词共现聚类分析结果自动判读和表达的研究[J]. 数据分析与知识发现, 2020, 4(9): 133-144.
[9] 温萍梅,叶志炜,丁文健,刘颖,徐健. 命名实体消歧研究进展综述*[J]. 数据分析与知识发现, 2020, 4(9): 15-25.
[10] 席运江, 杜蝶蝶, 廖晓, 仉学红. 基于超网络的企业微博用户聚类研究及特征分析*[J]. 数据分析与知识发现, 2020, 4(8): 107-118.
[11] 杨旭,钱晓东. 基于改进的Vicsek模型的社会网络同步聚类算法*[J]. 数据分析与知识发现, 2020, 4(4): 119-128.
[12] 熊回香,李晓敏,李跃艳. 基于图书评论属性挖掘的群组推荐研究*[J]. 数据分析与知识发现, 2020, 4(2/3): 214-222.
[13] 陈挺,王海名,王小梅. 基于可视化的基金资助热点及其演化发现方法研究*[J]. 数据分析与知识发现, 2020, 4(2/3): 60-67.
[14] 王晰巍,张柳,黄博,韦雅楠. 基于LDA的微博用户主题图谱构建及实证研究*——以“埃航空难”为例[J]. 数据分析与知识发现, 2020, 4(10): 47-57.
[15] 魏家泽,董诚,何彦青,刘志辉,彭柯芸. 基于均衡段落和分话题向量的新闻热点话题检测研究*[J]. 数据分析与知识发现, 2020, 4(10): 70-79.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 2015 《数据分析与知识发现》编辑部
地址:北京市海淀区中关村北四环西路33号 邮编:100190
电话/传真:(010)82626611-6626,82624938
E-mail:jishu@mail.las.ac.cn