Comparative evaluation of the Scatter Plot Matrix and Parallel Coordinates Plot Matrix

Hugh Garner, S. Fernstad
{"title":"Comparative evaluation of the Scatter Plot Matrix and Parallel Coordinates Plot Matrix","authors":"Hugh Garner, S. Fernstad","doi":"10.1109/IV56949.2022.00027","DOIUrl":null,"url":null,"abstract":"The Scatter Plot Matrix (SPLOM) and the Parallel Coordinates Plot Matrix (PCPM) are frequently used in exploratory data analysis for multivariate data to explore pairwise relationships, clustering and outliers. The SPLOM and PCPM are complex visualization methods with many potential interactions between data, task and visual representation. While numerous studies exist evaluating the SPLOM and Parallel Coordinates Plot (PCP) there is, to the best of our knowledge, no existing study evaluating the PCPM. This pilot study presents an evaluation of the performance of the SPLOM and PCPM for a set of common explorative tasks and identifies key directions for future work. The overall results indicate a minimal performance difference between the visualization methods for most tasks, but with significant variance between users, interactions between data features and response by method, and strong user preferences depending on task. As such, we recommend careful consideration of the background of potential users when choosing a method, and/or the use of complementary or linked views. Further work is required to understand the particular mechanisms impacting users' highly variable performance with the PCPM.","PeriodicalId":153161,"journal":{"name":"2022 26th International Conference Information Visualisation (IV)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 26th International Conference Information Visualisation (IV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IV56949.2022.00027","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The Scatter Plot Matrix (SPLOM) and the Parallel Coordinates Plot Matrix (PCPM) are frequently used in exploratory data analysis for multivariate data to explore pairwise relationships, clustering and outliers. The SPLOM and PCPM are complex visualization methods with many potential interactions between data, task and visual representation. While numerous studies exist evaluating the SPLOM and Parallel Coordinates Plot (PCP) there is, to the best of our knowledge, no existing study evaluating the PCPM. This pilot study presents an evaluation of the performance of the SPLOM and PCPM for a set of common explorative tasks and identifies key directions for future work. The overall results indicate a minimal performance difference between the visualization methods for most tasks, but with significant variance between users, interactions between data features and response by method, and strong user preferences depending on task. As such, we recommend careful consideration of the background of potential users when choosing a method, and/or the use of complementary or linked views. Further work is required to understand the particular mechanisms impacting users' highly variable performance with the PCPM.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
散点图矩阵与平行坐标图矩阵的比较评价
散点图矩阵(SPLOM)和平行坐标图矩阵(PCPM)在多变量数据的探索性数据分析中经常被使用,以探索成对关系、聚类和异常值。SPLOM和PCPM是复杂的可视化方法,在数据、任务和可视化表示之间存在许多潜在的相互作用。虽然已有许多研究对SPLOM和平行坐标图(PCP)进行了评价,但据我们所知,尚无对PCPM进行评价的研究。这项试点研究对SPLOM和PCPM的性能进行了评估,并为一系列共同的探索性任务确定了未来工作的关键方向。总体结果表明,对于大多数任务,可视化方法之间的性能差异很小,但在用户之间、数据特征之间的交互和方法响应之间、以及用户对任务的强烈偏好之间存在显著差异。因此,我们建议在选择方法时仔细考虑潜在用户的背景,和/或使用互补或链接视图。需要进一步的工作来理解影响PCPM高度可变的用户性能的特定机制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Phrase Features in Essay Report Sentences for Developing Critical Thinking Ability in a Fully Online Course Preoperative Image Segmentation for Organ Visualization Using Augmented Reality Technology During Open Liver Surgery Data. Information and Knowledge Visualization for Frequent Patterns VRGrid: Efficient Transformation of 2D Data into Pixel Grid Layout Augmenting the Reality of Situated Visualization
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1