Abstract：In the existing dimension reordering algorithms, the interactive functions allowing users to perform order navigation or reorder operations are rarely taken into account. Aiming at this problem, a navigation-guided correlation matrix is proposed for users to interactively reorder dimensions in radial visualization(RadViz). A hierarchical clustering algorithm with configurable parameters is specially designed for RadViz to recommend the initial dimension order. The dendrogram of results is employed to help users interactively reorder,select and delete dimensions for feature subset selection. Experiments show that the proposed method is interactive, user-friendly and helpful for alleviating the overlapping problem of data projections in RadViz.
[1] 袁晓如,郭翰琦,肖 何.高维数据可视化.中国计算机学会通讯, 2011, 7(4): 13-16. (YUAN X Y, GUO H Q, XIAO H. High-Dimensional Data Visua-lization. Communications of the CCF, 2011, 7(4): 13-16.) [2] HOFFMAN P, GRINSTEIN G, MARX K, et al. DNA Visual and Analytic Data Mining // Proc of the 8th Conference on Visualization. Los Alamitos, USA: IEEE, 1997: 437-442. [3] ARTERO A O, OLIVERIA M C F, LEVKOWITZ H. Enhanced High Dimensional Data Visualization through Dimension Reduction and Attribute Arrangement // Proc of the 10th International Confe-rence on Information Visualization. Washington, USA: IEEE, 2006: 707-712. [4] ALBUQUERQUE G, EISEMANN M, LEHMANN D J, et al. Improving the Visual Analysis of High-Dimensional Datasets Using Quality Measures // Proc of the IEEE Symposium on Visual Analytics, Science and Technology. Washington, USA: IEEE, 2010: 19-26. [5] YANG J, PENG W, WARD M O, et al. Interactive Hierarchical Dimension Ordering, Spacing and Filtering for Exploration of High Dimensional Datasets // Proc of the 9th Annual IEEE Conference on Information Visualization. Washington, USA: IEEE, 2003: 105-112. [6] LEBAN G, ZUPAN B, VIDMAR G, et al. VizRank: Data Visua-lization Guided by Machine Learning. Data Mining and Knowledge Discovery, 2006, 13(2): 119-136. [7] DI CARO L, FRIAS-MARTINEZ V, FRIAS-MARTINEZ E. Analyzing the Role of Dimension Arrangement for Data Visualization in Radviz // Proc of the 14th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining. Berlin, Germany: Springer, 2010: 125-132. [8] BINH H T T, VAN LONG T, HOAI N X, et al. Reordering Dimensions for Radial Visualization of Multidimensional Data-A Genetic Algorithms Approach // Proc of the IEEE Congress on Evolutionary Computation. Washington, USA: IEEE, 2014: 951-958. [9] ZHANG Z Y, MCDONNELL K T, ZADOK E, et al. Visual Correlation Analysis of Numerical and Categorical Data on the Correlation Map. IEEE Transactions on Visualization and Computer Graphics, 2015, 21(2): 289-303. [10] ANKERST M ,BERCHTOLD S, KEIM D A. Similarity Clustering of Dimensions for an Enhanced Visualization of Multidimensional Data // Proc of the IEEE Symposium on Information Visualization. Washington, USA: IEEE, 1998: 52-60. [11] ZHANG Z Y, MCDONNELL K T, MUELLER K. A Network-Based Interface for the Exploration of High-Dimensional Data Spaces // Proc of the IEEE Pacific Visualization Symposium. Washington, USA: IEEE, 2012: 17-24. [12] LI J, MARTENS J B, VAN WIJK J J. Judging Correlation from Scatterplots and Parallel Coordinate Plots. Information Visualization, 2010, 9(1): 13-30. [13] DUNN J C. Well-Separated Clusters and Optimal Fuzzy Partitions. Journal of Cybernetics, 1974, 4(1): 95-104. [14] BOX J F. Guinness, Gosset, Fisher, and Small Samples. Statistical Science, 1987, 2(1): 45-52.
2017, Vol. 30 
