Abstract
This paper presents the visualization of large datasets with SAMANN algorithm using clustering methods for initial dataset reduction for the network training. The visualization of multidimensional data is highly important in data mining because recent applications produce large amount of data that need specific means for the knowledge discovery. One of the ways to visualize multidimensional dataset is to project it onto a plane. This paper analyzes the visualization of multidimensional data using feed-forward neural network. We investigate an unsupervised backpropagation algorithm to train a multilayer feed-forward neural network (SAMANN) to perform the Sammon‘s nonlinear projection. The SAMANN network offers the generalization ability of projecting new data. Previous investigations showed that it is possible to train SAMANN using only a part of analyzed dataset without the loss of accuracy. It is very important to select proper vector subset for the neural network training. One of the ways to construct relevant training subset is to use clustering. This allows to speed up the visualization of large datasets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
Biswas, G., Jain, A.K., Dubes, R.C.: Evaluation of Projection Algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 3(6), 701–708 (1981)
Borg, I., Groenen, P.: Modern Multidimensional Scaling: Theory and Applications. Springer, Heidelberg (1997)
Dzemyda, G., Kurasova, O., Medvedev, V.: Dimension Reduction and Data Visualization Using Neural Networks. In: Emerging Artificial Intelligence Applications in Computer Engineering – Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies. Frontiers in Artificial Intelligence and Applications, vol. 160, pp. 25–49. IOS Press, Amsterdam (2007)
Faloutsos, C., Lin, K.: FastMap: a fast algorithm for indexing, datamining, and visualization. In: Proc. ACM SIGMOD, pp. 163–174 (1995)
Ivanikovas, S., Medvedev, V., Dzemyda, G.: Parallel Realizations of the SAMANN Algorithm. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds.) ICANNGA 2007. LNCS, vol. 4432, pp. 179–188. Springer, Heidelberg (2007)
Kohonen, T., Oja, E.: Visual Feature Analysis by the Self-Organising Maps. Neural Computing & Applications 7(3), 273–286 (1998)
Lee, R.C.T., Slagle, J.R., Blum, H.: A Triangulation Method for Sequential Mapping of Points from n-Space to Two-Space. IEEE Transactions on Computers 27, 288–299 (1977)
Lowe, D., Tipping, M.E.: Feed-forward Neural Networks and Topographic Mappings for Exploratory Data Analysis. Neural Computing and Applications 4, 83–95 (1996)
MacQueen, J.B.: Some Methods for classification and Analysis of Multivariate Observations. In: Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Press, Berkeley (1967)
Mao, J., Jain, A.K.: Artificial Neural Networks for Feature Extraction and Multivariate Data Projection. IEEE Trans. Neural Networks 6, 296–317 (1995)
Naud, A.: An Accurate MDS-Based Algorithm for the Visualization of Large Multidimensional Datasets. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 643–652. Springer, Heidelberg (2006)
de Ridder, D., Duin, R.P.W.: Sammon’s Mapping Using Neural Networks: A comparison. Pattern Recognition Letters 18, 1307–1316 (1997)
Sammon, J.W.: A Nonlinear Napping for Data Structure Analysis. IEEE Transactions on Computers 18, 401–409 (1969)
de Silva, V., Tenenbaum, J.B.: Global versus local methods in nonlinear dimensionality reduction. In: Becker, S., Thrun, S., Obermayer, K. (eds.) Proc. NIPS, vol. 15, pp. 721–728 (2003)
Wang, J.T.-L., Wang, X., Lin, K.-I., Shasha, D., Shapiro, B.A., Zhang, K.: Evaluating a class of distance-mapping algorithms for data mining and clustering. In: Proc. ACM KDD, pp. 307–311 (1999)
van Wezel, M.C., Kosters, W.A.: Nonmetric Multidimensional Scaling: Neural networks Versus Traditional Techniques. Intelligent Data Analysis 8(6), 601–613 (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ivanikovas, S., Dzemyda, G., Medvedev, V. (2008). Large Datasets Visualization with Neural Network Using Clustered Training Data. In: Atzeni, P., Caplinskas, A., Jaakkola, H. (eds) Advances in Databases and Information Systems. ADBIS 2008. Lecture Notes in Computer Science, vol 5207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85713-6_11
Download citation
DOI: https://doi.org/10.1007/978-3-540-85713-6_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85712-9
Online ISBN: 978-3-540-85713-6
eBook Packages: Computer ScienceComputer Science (R0)