Please wait a minute...
New Technology of Library and Information Service  2010, Vol. 26 Issue (7/8): 15-21    DOI: 10.11925/infotech.1003-3513.2010.07-08.04
article Current Issue | Archive | Adv Search |
Expressing Method and Empirical Research on the Use of Chernoff Faces to Represent Multi-dimensional Strategic Data
Wang Jianhai1,2  Zeng Zhen
1(School of Information Management,Wuhan University,Wuhan 430072,China)
2(Department of Computer Science,Huizhou University,Huizhou 516007,China)
Download: PDF(519 KB)   HTML  
Export: BibTeX | EndNote (RIS)      

According to the actual needs, the article puts forward a new method which applies moderately simplified Chernoff faces on multi-dimensional strategic data analysis. The strategy in the index variables distribution of Chernoff faces is the most important index distribution method based on entropy weight. Using improved Chernoff faces as visualization tool, the authors make a practical comparison analysis on some key economic strategic data in 2008 of three prefecture-level cities within the Pearl River Delta area. These cities are Huizhou, Zhongshan and Jiangmen. The research result shows that the method has the validity and practicality.

Key wordsChernoff faces      Economic index      Multiple statistical analysis     
Received: 04 May 2010      Published: 19 September 2010


Corresponding Authors: Wang Jianhai     E-mail:
About author:: Wang Jianhai Zeng Zhen

Cite this article:

Wang Jianhai Zeng Zhen. Expressing Method and Empirical Research on the Use of Chernoff Faces to Represent Multi-dimensional Strategic Data. New Technology of Library and Information Service, 2010, 26(7/8): 15-21.

URL:     OR

 [1] Chernoff H. The Use of Faces to Represent Points in K-dimensional Space Graphically[J]. Journal of the American Statistical Association,1973, 68(342): 361-368.
 [2] Huff D L, Mahajan V, Black W C. Facial Representation of Multivariate Data[J]. The Journal of Marketing,1981, 45(4): 53-59.
 [3] Song R, Zhao Z, Ou M. A Novel Clustering Method for Chernoff Faces Based on V-system[C]. In: Proceedings of the 2009 IEEE International Conference on Information and Automation, ICIA 2009.2009: 1556-1561.
 [4] Song R, Zhao Z, Wang X. The Application of V-system in Visualization of Multidimensional Data[C]. In: Proceedings of the 11th IEEE International Conference on Computer-Aided Design and Computer Graphics.2009: 170-173.
 [5] 王金甲,洪文学,李昕. 一种K-均值脸谱图聚类新算法[J]. 仪器仪表学报,2007, 28(10): 1916-1920.
 [6] 王金甲,李静,李昕,等. 着装脸谱图的分类新算法[J]. 燕山大学学报,2008, 32(5): 429-434.
 [7] 殷菲,潘晓平,吴震. Chernoff脸谱图的改进[J]. 中国卫生统计,2003, 20(4): 194-196.
 [8] Saxena  P C, Navaneetham K. The Effect of Cluster Size, Dimensionality, and Number of Clusters on Recovery of True Cluster Structure Through Chernoff-type Faces[J]. The Statistician,1991, 40(4): 415-425.
 [9] 任永功. 面向聚类的数据可视化方法及相关技术研究[D]. 沈阳: 东北大学, 2006.
[10] 方开泰. 多变量样本的图分析法(一)[J]. 数学的实践与认识,1981(3): 63-71.
[11] 洪文学,李听,徐永宏. 基于多元统计图表示原理的信息融合和模式识别技术[M]. 北京: 国防工业出版社, 2008: 100-103.
[12] Flury B, Riedwyl H. Graphical Representation of Multivariate Data by Means of Asymmetrical Faces[J]. Journal of the American Statistical Association,1981, 76(376): 757-765.
[13] 朱锦懋. Chernoff脸谱图的计算程序设计[J]. 福建林学院学报,1987, 7(1): 27-31.
[14] Dickson S. Chernoff Face[EB/OL].[2010-02-13].
[15] 邱菀华. 管理决策与应用熵学[M]. 北京: 机械工业出版社, 2002: 140-194.
[16] Morris C J, Ebert D S, Rheingans P. Experimental Analysis of the Effectiveness of Features in Chernoff Faces[C]. In: Proceedings of SPIE - The International Society for Optical Engineering.2000: 12-17.
[17] 黄亚钧. 宏观经济学[M]. 3版. 北京: 高等教育出版社, 2009: 26-41.

[1] Lv Yingjie, Fan Jing, Liu Jingfang. Authorship Identification of Chinese UGC Based on Stylistics[J]. 现代图书情报技术, 2013, 29(9): 48-53.
[2] Wang Hao, Zou Jieli, Deng Sanhong. Model Construction and Experiment Analysis of Automatic Indexing for Chinese Books[J]. 现代图书情报技术, 2013, 29(7/8): 55-62.
[3] Guo Shu. Research on Author Name Disambiguation Algorithm in the Literature Database[J]. 现代图书情报技术, 2013, 29(7/8): 69-74.
[4] Hu Changping, Chen Guo. A New Feature Selection Method Based on Term Contribution in Co-word Analysis[J]. 现代图书情报技术, 2013, 29(7/8): 89-93.
[5] Li Xiao, Ding Shengchun. Research on Review Spam Recognition[J]. 现代图书情报技术, 2013, 29(1): 63-68.
[6] Zeng Shaoqin, Wang Huilin, Zhang Yinsheng. Mandarin Text Representation Based on Minimal Recursion Semantics——Illustrate by Quantitative Noun Phrases[J]. 现代图书情报技术, 2012, (10): 35-41.
[7] Song Wen, Huang Jinxia, Liu Yi, Tang Yijie. SKE Key Technologies and Services for Knowledge Discovery[J]. 现代图书情报技术, 2012, 28(7): 13-18.
[8] Wang Li. Dynamic Faceted Method Based on Keyword Chains[J]. 现代图书情报技术, 2012, 28(7): 76-81.
[9] Liu Ping, Chen Ye. Survey of the State of the Art in Word Similarity[J]. 现代图书情报技术, 2012, 28(7): 82-89.
[10] Zhu Wenjing, Xia Cuijuan. Application of Two-dimensional Code in Library Mobile Service ——A Case of Shanghai Library[J]. 现代图书情报技术, 2012, 28(7): 115-120.
[11] Ma Jian, Du Zeyu, Li Shuqing. Personalized Book Recommendation Algorithm Based on Multi-interest Analysis in Library[J]. 现代图书情报技术, 2012, 28(6): 1-8.
[12] Jiang Hua, Su Xiaoguang. Chinese High-frequency Words Extraction Algorithm Without Thesaurus[J]. 现代图书情报技术, 2012, 28(6): 50-53.
[13] Xue Jianwu, Bai Yi. Study on the Relationship Storage of Ontology Topology Structure[J]. 现代图书情报技术, 2012, 28(5): 26-31.
[14] Xiao Jing, Liang Bing, Zhang Xiaodan, Lv Shijiong. Author Disambiguation Rules and Algorithm for Article Level Data[J]. 现代图书情报技术, 2012, 28(5): 55-59.
[15] Li Zhenqing, Liu Jianyi, Wang Cong, Wu Xu. Research and Implementation of Peer-review Experts Selection System[J]. 现代图书情报技术, 2012, 28(5): 81-86.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938