Abstract
Attempt to visualize high dimensional datasets typically encounter over plotting and decline in visual comprehension that makes the knowledge discovery and feature subset analysis difficult. Hence, reshaping the datasets using dimensionality reduction technique is paramount by removing the superfluous attributes to improve visual analytics. In this work, we applied rough set theory as dimensionality reduction and feature selection methods on visualization to facilitate knowledge discovery of multi-dimensional datasets. We provided the case study using real datasets and comparison against other methods to demonstrate the effectiveness of our approach.
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References
Fodor, I.K.: A survey of dimension reduction techniques. Technical Report. UCRL-ID-148494. Lawrence Livermore National Lab., 1-18 (2002)
Kruskal, J.B., Wish, M.: Multidimensional scaling. Sage Publications, Beverly Hills (1977)
Kohonen, T.: The self-organizing map. Neurocomputing 21(1-3), 1–6 (1998)
Johansson, S., Johansson, J.: Interactive dimensionality reduction through user-defined combinations of quality metrics. IEEE Transaction on Visualization and Computer Graphics 15(6), 993–1000 (2009)
Choo, J., Bohn, S., Park, H.: Two-stage framework for visualization of clustered high dimensional data. In: Proc. of IEEE Symposium on VAST, pp. 67–74 (2009)
Inselberg, A.: The plane with parallel coordinates. The Visual Computer 1(2), 69–91 (1985)
Pawlak, Z.: Rough Set: Theoretical aspects of reasoning about data. Kluwer, Netherlands (1991)
Ziarko, W.: Variable precision rough set model. J. Comp. & Sys. Sci. 46(1), 39–59 (1993)
Tsumoto, S.: Accuracy and Coverage in Rough Set Rule Induction. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 373–380. Springer, Heidelberg (2002)
Yao, Y., Zhao, Y.: Attribute reduction in decision-theoretic rough set models. Information Sciences 178(1), 3356–3373 (2008)
Ankerst, M., Berchtold, S., Keim, D.A.: Similarity clustering of dimensions for an enhanced visualization of multidimensional data. In: Proc. of IEEE Symposium on Information Visualization, pp. 52–60 (1998)
Saporta, G.: Some simple rules for interpreting outputs of principal components and correspondence analysis. In: Proc. of ASMDA 1999. University of Lisbon (1999)
Cattell, R.B.: The scree test for the number of factors. Multivariate behavioral research 1(2), 245–276 (1966)
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Huang, TH., Huang, M.L., Jin, J.S. (2011). Parallel Rough Set: Dimensionality Reduction and Feature Discovery of Multi-Dimensional Data in Visualization. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24958-7_12
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DOI: https://doi.org/10.1007/978-3-642-24958-7_12
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