Abstract
Numerical data mining is a task for which several techniques have been developed that can provide a quick insight into a practical problem, if an easy to use common software platform is available. VISRED- Data Visualisation by Space Reduction presented here, aims to be such a tool for data classification and clustering. It allows the quick application of Principal Component Analysis, Nonlinear Principal Component Analysis, Multi-dimensional Scaling (classical and non classical). For clustering several techniques have been included: hierarchical, k-means, subtractive, fuzzy k-means, SOM- Self Organizing Map (batch and recursive versions). It reads from and writes to Excel sheets. Its utility is shown with two applications: the visbreaker process part of an oil refinery and the UCI benchmark problem of breast cancer diagnosis.
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References
de Oliveira, M.C.F., Levkowitz, H.: From visual data exploration to visual data mining: A survey. IEEE Trans on Visualization and Computer Graphics 9(3), 378–394 (2003)
Borg, I., Groenen, P.: Modern Multidimensional Scaling, Theory and Applications, 2nd edn. Springer, Heidelberg (2005)
Young, G., Householder, A.S.: A note on multidimensional psycho-physical analysis. Psychometrika 6, 331–333 (1941)
Cox, T.F.: Multidimensional Scaling in Process Control. In: Khattree, R. (ed.) Handbook of Statistics 22. Statistics in Industry, North Holland (2003)
Cox, F.T.: Multidimensional Scaling, 2nd edn. Chapman and Hall CRC Press, New York (2001)
Matheus, J., Dourado, A., Henriques, J.: Iterative Multidimensional Scaling for Industrial Process Monitoring. Trans. IEEE SMC Int. Conf., Seoul, Taiwan, October 8-711 (2006)
Jolliffe, I.T.: Principal Component Analysis, 2nd edn. Springer, Heidelberg (October 2002)
Hsieh, W.W.: Neuralnets for Multivariable and Time Series Analysis (NeuMATSA): A User Manual, www.ocgy.ubc.ca/~william/Pubs/NN.manual.pdf
Kramer, M.A.: Nonlinear Principal Component Analysis Using Autoassociative Neural Networks. AIChE Journal 37(2), 233–243 (1991)
Daszykowski, B., Walczak, 1., Massart, D.L.: A journey into low-dimensional spaces with autoassociative neural networks. In: Talanta, vol. 59, pp. 1095–1105. Elsevier, Amsterdam (2003)
Thomopoulos, S.C.A., Bougoulias, D.K, Wann, C.-D.: Dignet an Unsupervised-Learning Clustering Algorithm for Clustering and Data Fusion. IEEE Trans On Aerospace and Electronic Systems 31(1) (1995)
Wann, C.-D., Thomopoulos, S.C.A.: A comparative study of self-organizing clustering algorithms Dignet and ART2. Neural Networks 10(4), 737 (1997)
Kohonen, T.: Self-Organizing Maps, 3rd Extended edn. Springer Series in Information Sciences, vol. 30. Springer, Heidelberg (2001)
Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: SOM Toolbox for Matlab 5. Helsinki University of Technology (2000)
Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: Self-organizing map in Matlab: the SOM Toolbox. In: Proceedings of the Matlab DSP Conference, Espoo, Finland, November 16-17, 1999, pp. 35–40 (1999)
Voegtlin, T.: Recursive self-organizing maps. Neural Networks 15, 979–991 (2002)
Hagenbuchner, M., Sperduti, A., Tsoi, A.C.: A self-organizing map for adaptive processing of structured data. IEEE Transactions on Neural Networks 14(3), 491–505 (2003)
Saffari, A.R., Alamdari, A.: Recurrent Self-Organizing Map (RSOM) Toolbox for MATLAB (July 2005), http://www.ymer.org/amir/software/recurrent-self-organizing-maps
Galp data, Sines Refinery (2006)
Murphy, P.M.: UCI Repository of Machine Learning Databases and Domain Theories (on-line), avaiable at http://www.ics.uci.edu/~mlearn/MLRepository.html
Wolberg, W.H., Street, W.N., Mangasarian, O.L.: Machine learning techniques to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77, 163–171 (1994)
Matlab is a trademark of Mathworks, Inc.,
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Dourado, A., Ferreira, E., Barbeiro, P. (2007). VISRED –Numerical Data Mining with Linear and Nonlinear Techniques. In: Perner, P. (eds) Advances in Data Mining. Theoretical Aspects and Applications. ICDM 2007. Lecture Notes in Computer Science(), vol 4597. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73435-2_8
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DOI: https://doi.org/10.1007/978-3-540-73435-2_8
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