ScagExplorer: Exploring Scatterplots by Their Scagnostics

被引:60
作者
Tuan Nhon Dang [1 ]
Wilkinson, Leland [2 ]
机构
[1] Univ Illinois, Chicago, IL 60680 USA
[2] Univ Illinois, Skytree Software Inc, Chicago, IL 60680 USA
来源
2014 IEEE PACIFIC VISUALIZATION SYMPOSIUM (PACIFICVIS) | 2014年
关键词
I.5.2 [Pattern recognition]: Design MethodologyPattern analysis; VIEWS;
D O I
10.1109/PacificVis.2014.42
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A scatterplot displays a relation between a pair of variables. Given a set of nu variables, there are nu(nu-1)/2 pairs of variables, and thus the same number of possible pairwise scatterplots. Therefore for even small sets of variables, the number of scatterplots can be large. Scatterplot matrices (SPLOMs) can easily run out of pixels when presenting high-dimensional data. We introduce a theoretical method and a testbed for assessing whether our method can be used to guide interactive exploration of high-dimensional data. The method is based on nine characterizations of the 2D distributions of orthogonal pairwise projections on a set of points in multidimensional Euclidean space. Working directly with these characterizations, we can locate anomalies for further analysis or search for similar distributions in a "large" SPLOM with more than a hundred dimensions. Our testbed, ScagExplorer, is developed in order to evaluate the feasibility of handling huge collections of scatterplots.
引用
收藏
页码:73 / 80
页数:8
相关论文
共 28 条
[1]  
Agrawal R., 1998, SIGMOD Record, V27, P94, DOI 10.1145/276305.276314
[2]  
Albuquerque G., 2011, 2011 IEEE Conference on Visual Analytics Science and Technology, P13, DOI 10.1109/VAST.2011.6102437
[3]   Low-level components of analytic activity in information visualization [J].
Amar, R ;
Eagan, J ;
Stasko, J .
INFOVIS 05: IEEE SYMPOSIUM ON INFORMATION VISUALIZATION, PROCEEDINGS, 2005, :111-117
[4]  
Anand A, 2012, IEEE CONF VIS ANAL, P43, DOI 10.1109/VAST.2012.6400490
[5]   Similarity clustering of dimensions for an enhanced visualization of multidimensional data [J].
Ankerst, M ;
Berchtold, S ;
Keim, DA .
IEEE SYMPOSIUM ON INFORMATION VISUALIZATION - PROCEEDINGS, 1998, :52-+
[6]  
[Anonymous], 1988, BIOMETRICS
[7]  
[Anonymous], 1978, Multidimensional scaling
[8]  
[Anonymous], 2004, SIGKDD EXPLOR, DOI DOI 10.1145/1007730.1007731
[9]  
[Anonymous], 2007, Uci machine learning repository
[10]  
Assent I., 2007, ACM SIGKDD Explorations Newsletter, V9, P5, DOI DOI 10.1145/1345448.1345451