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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (1): 91-100    DOI: 10.11925/infotech.2096-3467.2021.0858
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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
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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     
Received: 19 August 2021      Published: 22 February 2022
ZTFLH:  TP391  
Fund:National Natural Science Foundation of China(72074020)
Corresponding Authors: Wang Xuefeng,ORCID:0000-0002-4857-6944     E-mail: wxf122@bit.edu.cn

Cite this article:

Wang Xuefeng, Ren Huichao, Liu Yuqin. Visualization Method for Technology Theme Map with Clustering. Data Analysis and Knowledge Discovery, 2022, 6(1): 91-100.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.0858     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I1/91

Screen Lattice Division and Density Function Calculation Rang Diagram
Technology Theme Map in the Form of Heat Map
Technology Theme Map in the Form of Density
Technology Theme Map in the Form of Multicolor Topographic Map
Technology Theme Map in the Form of Monochrome Topographic Map
Technology Theme Map Integrating Clustering Information (General Color Intensity Function)
Technology Theme Map Integrating Clustering Information (Power Function Color Intensity Function)
Quantum Cryptographic Communication Technology Theme Map in the Form of Clustering
Quantum Cryptographic Communication Technology Theme Map in the Form of Heat Map
Quantum Cryptographic Communication Technology Theme Map in the Form of Multi-colored Topography
[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.)
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