Network-Based Interface for the Exploration of High-Dimensional Data Spaces

被引:0
|
作者
Zhang, Zhiyuan [1 ]
McDonnell, Kevin T. [2 ]
Mueller, Klaus [1 ]
机构
[1] SUNY Stony Brook, Dept Comp Sci, Visual Analyt & Imaging Lab, Stony Brook, NY 11794 USA
[2] Dept Math & Comp Sci, Dowling College, NY USA
基金
美国国家科学基金会;
关键词
Visual analytics; parallel coordinates; multivariate data; correlation; network-based; linked displays;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
The navigation of high-dimensional data spaces remains challenging, making multivariate data exploration difficult. To be effective and appealing for mainstream application, navigation should use paradigms and metaphors that users are already familiar with. One such intuitive navigation paradigm is interactive route planning on a connected network. We have employed such an interface and have paired it with a prominent high-dimensional visualization paradigm showing the N-D data in undistorted raw form: parallel coordinates. In our network interface, the dimensions form nodes that are connected by a network of edges representing the strength of association between dimensions. A user then interactively specifies nodes/edges to visit, and the system computes an optimal route, which can be further edited and manipulated. In our interface, this route is captured by a parallel coordinate data display in which the dimension ordering is configured by the specified route. Our framework serves both as a data exploration environment and as an interactive presentation platform to demonstrate, explain, and justify any identified relationships to others. We demonstrate our interface within a business scenario and other applications.
引用
收藏
页码:17 / 24
页数:8
相关论文
共 50 条
  • [1] Network-based Clustering and Embedding for High-Dimensional Data Visualization
    Zhang, Hengyuan
    Chen, Xiaowu
    [J]. 2013 INTERNATIONAL CONFERENCE ON COMPUTER-AIDED DESIGN AND COMPUTER GRAPHICS (CAD/GRAPHICS), 2013, : 290 - 297
  • [2] Network-based regularization for analysis of high-dimensional genomic data with group structure
    Kim, Kipoong
    Choi, Jiyun
    Sun, Hokeun
    [J]. KOREAN JOURNAL OF APPLIED STATISTICS, 2016, 29 (06) : 1117 - 1128
  • [3] A Network-Based Model for High-Dimensional Information Filtering
    Nanas, Nikolaos
    Vavalis, Manolis
    De Roeck, Anne
    [J]. SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, 2010, : 202 - 209
  • [4] Exploration of High-Dimensional Nuclei Data
    Fuentes, Fernando
    Kettani, Houssain
    Ostrouchov, George
    Stoitsov, Mario
    Nam, Hai Ah
    [J]. 2010 2ND INTERNATIONAL CONFERENCE ON COMPUTER AND AUTOMATION ENGINEERING (ICCAE 2010), VOL 4, 2010, : 1 - 4
  • [5] Robust network-based regularization and variable selection for high-dimensional genomic data in cancer prognosis
    Ren, Jie
    Du, Yinhao
    Li, Shaoyu
    Ma, Shuangge
    Jiang, Yu
    Wu, Cen
    [J]. GENETIC EPIDEMIOLOGY, 2019, 43 (03) : 276 - 291
  • [6] Research on deep neural network-based anomaly detection technology in high-dimensional data environment
    Wang, Yan
    [J]. Applied Mathematics and Nonlinear Sciences, 2024, 9 (01)
  • [7] Clustering in high-dimensional data spaces
    Murtagh, FD
    [J]. STATISTICAL CHALLENGES IN ASTRONOMY, 2003, : 279 - 292
  • [8] Network-based regularization for matched case-control analysis of high-dimensional DNA methylation data
    Sun, Hokeun
    Wang, Shuang
    [J]. STATISTICS IN MEDICINE, 2013, 32 (12) : 2127 - 2139
  • [9] Graph convolutional network-based feature selection for high-dimensional and low-sample size data
    Chen, Can
    Weiss, Scott T.
    Liu, Yang-Yu
    [J]. BIOINFORMATICS, 2023, 39 (04)
  • [10] Hiding outliers in high-dimensional data spaces
    Steinbuss G.
    Böhm K.
    [J]. International Journal of Data Science and Analytics, 2017, 4 (3) : 173 - 189