标题:Evaluating Multi-Dimensional Visualizations for Understanding Fuzzy Clusters
作者:Zhao, Ying; Luo, Feng; Chen, Minghui; Wang, Yingchao; Xia, Jiazhi; Zhou, Fangfang; Wang, Yunhai; Chen, Yi; Chen, Wei
作者机构:[Zhao, Ying; Luo, Feng; Chen, Minghui; Wang, Yingchao; Xia, Jiazhi; Zhou, Fangfang] Cent S Univ, Changsha, Hunan, Peoples R China.; [Wang, Yunhai] S 更多
通讯作者:Xia, Jiazhi;Zhao, Y
通讯作者地址:[Zhao, Y]Cent S Univ, Changsha, Hunan, Peoples R China.
来源:IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
出版年:2019
卷:25
期:1
页码:12-21
DOI:10.1109/TVCG.2018.2865020
关键词:Evaluation; multi-dimensional visualization; fuzzy clustering; parallel; coordinate plot; scatterplot matrix; principal; component analysis;; radviz
摘要:Fuzzy clustering assigns a probability of membership for a datum to a cluster, which veritably reflects real-world clustering scenarios but significantly increases the complexity of understanding fuzzy clusters. Many studies have demonstrated that visualization techniques for multi-dimensional data are beneficial to understand fuzzy clusters. However, no empirical evidence exists on the effectiveness and efficiency of these visualization techniques in solving analytical tasks featured by fuzzy clusters. In this paper, we conduct a controlled experiment to evaluate the ability of fuzzy clusters analysis to use four multi-dimensional visualization techniques, namely, parallel coordinate plot, scatterplot matrix, principal component analysis, and Radviz. First, we define the analytical tasks and their representative questions specific to fuzzy clusters analysis. Then, we design objective questionnaires to compare the accuracy, time, and satisfaction in using the four techniques to solve the questions. We also design subjective questionnaires to collect the experience of the volunteers with the four techniques in terms of ease of use, informativeness, and helpfulness. With a complete experiment process and a detailed result analysis, we test against four hypotheses that are formulated on the basis of our experience, and provide instructive guidance for analysts in selecting appropriate and efficient visualization techniques to analyze fuzzy clusters.
收录类别:EI;SCOPUS;SCIE
Scopus被引频次:3
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052635059&doi=10.1109%2fTVCG.2018.2865020&partnerID=40&md5=88cc731e80a92cc2ccb3da76883b81b1
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