Evaluation of Multivariate Visualization on a Multivariate Task
Multivariate visualization techniques have attracted great interest as the dimensionality of data sets grows. One premise of such techniques is that simultaneous visual representation of multiple variables will enable the data analyst to detect patterns amongst multiple variables. Such insights coul...
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Published in | IEEE transactions on visualization and computer graphics Vol. 18; no. 12; pp. 2114 - 2121 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
United States
IEEE
01.12.2012
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
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Abstract | Multivariate visualization techniques have attracted great interest as the dimensionality of data sets grows. One premise of such techniques is that simultaneous visual representation of multiple variables will enable the data analyst to detect patterns amongst multiple variables. Such insights could lead to development of new techniques for rigorous (numerical) analysis of complex relationships hidden within the data. Two natural questions arise from this premise: Which multivariate visualization techniques are the most effective for high-dimensional data sets? How does the analysis task change this utility ranking? We present a user study with a new task to answer the first question. We provide some insights to the second question based on the results of our study and results available in the literature. Our task led to significant differences in error, response time, and subjective workload ratings amongst four visualization techniques. We implemented three integrated techniques (Data-driven Spots, Oriented Slivers, and Attribute Blocks), as well as a baseline case of separate grayscale images. The baseline case fared poorly on all three measures, whereas Datadriven Spots yielded the best accuracy and was among the best in response time. These results differ from comparisons of similar techniques with other tasks, and we review all the techniques, tasks, and results (from our work and previous work) to understand the reasons for this discrepancy. |
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AbstractList | Multivariate visualization techniques have attracted great interest as the dimensionality of data sets grows. One premise of such techniques is that simultaneous visual representation of multiple variables will enable the data analyst to detect patterns amongst multiple variables. Such insights could lead to development of new techniques for rigorous (numerical) analysis of complex relationships hidden within the data. Two natural questions arise from this premise: Which multivariate visualization techniques are the most effective for high-dimensional data sets? How does the analysis task change this utility ranking? We present a user study with a new task to answer the first question. We provide some insights to the second question based on the results of our study and results available in the literature. Our task led to significant differences in error, response time, and subjective workload ratings amongst four visualization techniques. We implemented three integrated techniques (Data-driven Spots, Oriented Slivers, and Attribute Blocks), as well as a baseline case of separate grayscale images. The baseline case fared poorly on all three measures, whereas Datadriven Spots yielded the best accuracy and was among the best in response time. These results differ from comparisons of similar techniques with other tasks, and we review all the techniques, tasks, and results (from our work and previous work) to understand the reasons for this discrepancy. |
Author | Livingston, M. A. Zhuming Ai Decker, J. W. |
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Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Dec 2012 |
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References | bibttg201212211423 bibttg201212211412 bokinsky (bibttg20121221142) 2003 miller (bibttg201212211417) 2007 bibttg201212211411 bibttg201212211410 bibttg201212211420 hagen (bibttg20121221144) 1994 livingston (bibttg201212211415) 2012 ware (bibttg201212211421) 2004 bibttg20121221141 joshi (bibttg20121221149) 2007 bibttg20121221143 hart (bibttg20121221146) 1988 bibttg20121221145 bibttg201212211416 bibttg20121221147 weigle (bibttg201212211422) 2000 bibttg20121221148 bibttg201212211414 bibttg201212211413 bibttg201212211419 bibttg201212211418 |
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SubjectTerms | Analysis of variance Data visualization Gray-scale Image color analysis Mathematical analysis Mathematical models multivariate visualization Quantitative evaluation Response time Shape analysis Spots Studies Tasks texture perception Time factors visual task design Visualization |
Title | Evaluation of Multivariate Visualization on a Multivariate Task |
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