标题:Column-based cluster and bar axis density in parallel coordinates
作者:Tang, Lei ;Li, Xue-Qing ;Qi, Wen-Jing ;Jiang, Zhi-Fang
通讯作者:Tang, L
作者机构:[Tang, Lei ;Li, Xue-Qing ;Qi, Wen-Jing ;Jiang, Zhi-Fang ] School of Computer Science and Technology, Shandong University, Jinan 250101, China
会议名称:3rd International Symposium on Visual Information Communication, VINCI 2010
会议日期:28 September 2010 through 29 September 2010
来源:VINCI 2010: 3rd Visual Information Communication - International Symposium
出版年:2010
DOI:10.1145/1865841.1865853
关键词:Brand; Cluster; Density; Information visualization; K-means; Parallel coordinates
摘要:In this paper we organize multi-dimensional datasets with column-based approach instead of the traditional row-based method, each column referring to one dimension and we use bar axis in place of line axis to represent corresponding dimension. Then parallel coordinates with column-based cluster, bar axis density and other techniques is used to convey a large complex multi-dimensional dataset in a relative small screen through the following steps: (a) visualization of column-based clusters with user-defined granularity to simplify the corresponding dimension where we group all the data points into several discrete values; (b) several distinct colors to distinguish the lines contain different amount of data points; (c) opacity is introduced to visualization to tell the difference among the lines with the same color; (d) brand instead of polyline to reveal the centre and the extent of each cluster; (e) layer-based drawing technique to emphasize the heavy lines and to denote the trend of multi-dimensional datasets; (f) bar axis to provide special space to illustrate the density of the dataset on each axis. Anyway, our work has two primary goals: one is to convey large dataset with legible compact vivid visualization on a limited screen area. The other one is to simultaneously reveal as many information features as possible away from clutter. Copyright © 2010 ACM.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-78649813437&doi=10.1145%2f1865841.1865853&partnerID=40&md5=5d03aff5d9bebf05248f782e672767f1
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