Abstract
Visualization of high-dimensional data is an important issue in data mining as it enhances the chance to selectively choose appropriate techniques for analyzing data. In this paper, two extensions to recent angle based multi-dimensional scaling techniques are presented. The first approach concerns the preprocessing of the data with the objective to lower the error of the subsequent mapping. The second aims at improving differentiability of angle based mappings by augmenting the target space by one additional dimension. Experimental results demonstrate the gain of efficiency in terms of layout quality and computational complexity.
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Rehm, F., Klawonn, F., Kruse, R.: Mds polar - a new approach for dimension reduction to visualize high dimensional data. In: Famili, A.F., Kok, J.N., Pena, J.M., Siebes, A., Feelders, A. (eds.) IDA 2005. LNCS, vol. 3646, pp. 316–327. Springer, Heidelberg (2005)
Rehm, F., Klawonn, F., Kruse, R.: Polarmap - a new approach to visualisation of high dimensional data. In: IEEE Proceedings of the Tenth International Conference on Information Visualisation (IV 2006), London, pp. 731–740 (2006)
Chalmers, M.: A linear iteration time layout algorithm for visualising high-dimensional data. In: Proceedings of IEEE Visualization 1996, San Francisco, CA, pp. 127–132 (1996)
Faloutsos, C., Lin, K.: A fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets. In: Proceedings of ACM SIGMOD International Conference on Management of Data, San Jose, CA, pp. 163–174 (1995)
Morrison, A., Ross, G., Chalmers, M.: Fast multidimensional scaling through sampling, springs and interpolation. Information Visualization 2, 68–77 (2003)
Pekalska, E., Ridder, D.D., Duin, R.P.W., Kraaijveld, M.A.: A new method of generalizing sammon mapping with application to algorithm speed-up. In: Boasson, M., Kaandorp, J.A., Tonino, J.F.M., Vosselman, M.G. (eds.) Proceedings of the 5th Annual Conference of the Advanced School for Computing and Imaging (ASCI 1999), pp. 221–228 (1999)
Williams, M., Munzner, T.: Steerable, progressive multidimensional scaling. In: Proceedings the 10th IEEE Symposium on Information Visualization, Austin, TX, pp. 57–64 (2004)
Sammon, J.W.: A nonlinear mapping for data structure analysis. IEEE Transactions Computer C-18, 401–409 (1969)
Fisher, R.A.: The use of multiple measurements in taxonomic problems. Annual Eugenics 7, Part II, 179–188 (1936)
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Rehm, F., Klawonn, F. (2008). Improving Angle Based Mappings. In: Tang, C., Ling, C.X., Zhou, X., Cercone, N.J., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2008. Lecture Notes in Computer Science(), vol 5139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88192-6_3
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DOI: https://doi.org/10.1007/978-3-540-88192-6_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88191-9
Online ISBN: 978-3-540-88192-6
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