A new variant of radial visualization for supervised visualization of high dimensional data
Email:
vtran@utc.edu.vn
Từ khóa:
Radial visualization, high-dimensional data, quality visualization.
Tóm tắt
Radial Visualization technique is a non linear dimensionality reduction method. Radial Visualization projects multivariate data in the 2-dimensional visual space inside the unit circle. Radial Visualization supports display both the samples and the attributes that provides useful information of data structures. In this article, we introduced a new variant of Radial Visualization for visualizing high dimensional data set that named Arc Radial Visualization. The new proposal that modified Radial Visualization supported more space to display high dimensional datasets. Our method provides an improvement in visualizing cluster structures of high dimensional data sets on the Radial Visualization. We present our proposal method with two quality measurements and proved the effectiveness of our approach for several real datasets.Tài liệu tham khảo
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[23] V.L. Tran, Another look at radial visualization for class-preserving multivariate data visualization, Informatica (Slovenia), 41 (2017) 159–168.
[24] F. Zhou, W. Huang, J. Li, Y. Huang, Y. Shi, Y. Zhao, Extending dimensions in radviz based on mean shift, In IEEE Pacific Visualization Symposium (PacificVis), Hangzhou, China, 111–115, 2015. https://doi.org/10.1109/PACIFICVIS.2015.7156365
[2] I. B. Correa, A. de Carvalho, Dual-radviz, Preserving context between classification evaluation and data exploration with radviz, In Proceedings 5th Brazilian Conference on Intelligent Systems, Recife, Brazil, 2016, 241–246. DOI: 10.1109/BRACIS.2016.052
[3] K. Daniels, G. Grinstein, A. Russell, M. Glidden, Properties of normalized radial visualizations, Information Visualization, 11 (2012) 273–300.
[4] J. Demsar, G. Leban, B. Zupan, Freeviz, An intelligent multivariate visualization approach to explorative analysis of biomedical data, Journal of Biomedical Informatics, 40 (2007) 661–671.
[5] L. di Caro, V. Frias-Martinez, E. Frias-Martinez, Analyzing the role of dimension arrangement for data visualization in radviz, In Pacific-Asia Conference on Knowledge Discovery and Data Mining, Advances in Knowledge Discovery and Data Mining, Hyderabat, India, 2010, 125–132.
[6] G. Dzemyda, O. Kurasova, J. Zilinskas, Multidimensional Data Visualization: Methods and Applications, Springer Publishing Company, Incorporated, 2012.
[7] U. Fayyad, G. Grinstein, A. Wierse, Information Visualization in Data Mining and Knowledge Discovery, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2002.
[8] G. Grinstein, M. Trutschl, U. Cvek, High-dimensional visualizations, In Proceedings of the Visual Data Mining KDD Workshop 2001, 2 (2001) 7–19.
[9] P. Hoffman, G. Grinstein, K. Marx, I. Grosse, E. Stanley, DNA visual and analytic data mining, In Proceedings of the 8th conference on Visualization’97, pp. 437–441. IEEE Computer Society Press, 1997.
[10] P. Hoffman, G. Grinstein, D. Pinkney, Dimensional anchors: a graphic primitive for multidimensional multivariate information visualizations, In Proceedings of the 1999 workshop on new paradigms in information visualization and manipulation, 9–16, 1999. https://doi.org/10.1145/331770.331775
[11] E. Kandogan, Star coordinates, A multidimensional visualization technique with uniform treatment of dimensions, In Proceedings of the IEEE Information Visualization Symposium, Hot Topics, 4–8, 2000.
[12] E. Kandogan. Visualizing multi-dimensional clusters, trends, and outliers using star coordinates, In Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 107–116, 2001.
[13] G. Leban, B. Zupan, G. Vidmar, I. Bratko, VizRank, Data visualization guided by machine learning, Data Mining and Knowledge Discovery, 13 (2006) 119–136. https://doi.org/10.1007/s10618-005-0031-5
[14] D.J. Lehmann, H. Theisel, General projective maps for multidimensional data projection, Computer Graphics Forum, 35 (2016) 443–453. https://doi.org/10.1111/cgf.12845
[15] S. Liu, D. Maljovec, B. Wang, P.T. Bremer, V. Pascucci, Visualizing highdimensional data: Advances in the past decade, IEEE Transactions on Visualization and Computer Graphics, 23 (2017) 1249–1268. https://doi.org/10.1109/TVCG.2016.2640960
[16] J.F. Mccarthy, K.A. Marx, P. Hoffman, A.G. Gee, P. O’neil, M.L. Ujwal, J. Hotchkiss, Applications of machine learning and high-dimensional visualization in cancer detection, diagnosis and management, Annals of the New York Academy of Sciences, 1020 (2004) 239–262.
[17] J.H. Ono, F. Sikansi, D.C. Corrêa, F.V. Paulovich, A. Paiva, L. G. Nonato, Concentric radviz: visual exploration of multi-task classification, In Conference on Graphics, Patterns and Images, 165–172, 2015
[18] M. Erik, H. Pedersen, Good parameters for differential evolution, Technical Report HL1002, Hvass Laboratories, 2010.
[19] K. Price, R.M. Storn, J.A. Lampinen, Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series), Springer-Verlag New York, Inc., 2005.
[20] M.R. Sanchez, L. Raya, F. Diaz, A. Sanchez, A comparative study between radviz and star coordinates, IEEE transactions on visualization and computer graphics, 22 (2016) 619–628. http://dx.doi.org/10.1109/TVCG.2015.2467324
[21] A. Russell, K. Daniels, G. Grinstein, Voronoi diagram based dimensional anchor assessment for radial visualizations, In 16th International Conference on Information Visualisation, Vienna, Austria 229–233, 2012.
[22] A. Russell, R. Marceau, F. Kamayou, K. Daniels, G. Grinstein, Clustered data separation via barycentric radial visualization, In Proceedings of the 2014 International Conference on Modeling, Simulation and Visualization Methods (MSV), Las Vegas, USA, 101-107, 2014.
[23] V.L. Tran, Another look at radial visualization for class-preserving multivariate data visualization, Informatica (Slovenia), 41 (2017) 159–168.
[24] F. Zhou, W. Huang, J. Li, Y. Huang, Y. Shi, Y. Zhao, Extending dimensions in radviz based on mean shift, In IEEE Pacific Visualization Symposium (PacificVis), Hangzhou, China, 111–115, 2015. https://doi.org/10.1109/PACIFICVIS.2015.7156365
Tải xuống
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Nhận bài
22/07/2019
Nhận bài sửa
16/08/2019
Chấp nhận đăng
16/08/2019
Xuất bản
15/11/2019
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Kiểu trích dẫn
Tran Van, L., & Thi, N. (1573750800). A new variant of radial visualization for supervised visualization of high dimensional data. Tạp Chí Khoa Học Giao Thông Vận Tải, 70(3), 162-172. https://doi.org/10.25073/tcsj.70.3.24
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