Abstract
Awareness about interconnectivities and interactions among parameters is vital for the identification of the optimal manufacturing routes and economic factors within a manufacturing system. Within this context, multidimensional data projection methods, Principal Component Mapping (PCM) and Sammon’s Mapping, have been scrutinized for visualizing multivariate interaction patterns. As a new approach, these techniques were employed in such a way that interactive multi-layer maps could be created. Each layer within the generated map matches to a specific attribute and characteristic of the dataset. Individual layers within the map can be interactively selected and superimposed to show multiple and partial interactions.
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References
Ralston, P., DePuy, G., Graham, J.H.: Computer-based Monitoring and Fault Diagnosis: Chemical Process Case Study. ISA Transactions 40, 85–98 (2001)
Jackson, J.E.: A User Guide to Principal Components. John Wiley & Sons, Inc Chichester (1991)
Aggarwal, C.C.: On the Effects of Dimensionality Reduction on High Dimensional Similarity Search, IBM T.J. Watson Research Centre, Yorktown Heights NY 10598
Bai, Z., Demmel, J., et al.: Templates for the solution of algebraic Eigenvalue Problems: a practical guide. Siam (2000)
Vesanto, J.: SOM-Based Data Visualization Methods. Intell. Data Anal. 3(2) (1999)
Jolliffe, I.T.: Principal Component Analysis, pp. 41–46. Springer, Heidelberg (1986)
Sammon, J.W.: A non-linear mapping for data structure analysis. IEEE Transactions on Computers 18, 401–409 (1969)
Rider, D.D., Duin, R.P.: Pattern Recognition Letters 18, 1307–1316 (1997)
Biswas, G., Jain, A.K., Dubes, R.C.: Evaluation of Projection algorithms. IEEE Trans. Pattern AnalMach. Intell. 3(6), 701–708 (1981)
Bezedek, J.C., Pal, N.R.: An index of topological preservation for feature extraction. Pattern Recognition 28, 381–391 (1995)
Kohonen, T.: Self-Organizing Maps, 2nd edn. Springer, Heidelberg (1997)
Dzemyda, G.: Visualisation of a set of parameters characterized by their correlation matrix. Computational Statistics & Data Analysis (Elsevier) 36, 15–30 (2001)
Mao, J., Jain, A.: Artificial Neural Networks for feature extraction and multivariate data projection. IEEE Transactions on Neural Networks 6, 296–317 (1995)
Lerner, B., et al.: Pattern Analysis & Applications, vol. 3, pp. 61–68. Springer, Heidelberg (2000)
Lerner, B., et al.: On Pattern Classification with Sammon’s Nonlinear Mapping – An Experiemental Study. Pattern Recognition (Pergamon) 31(4), 371–381 (1998)
Lee, R.C.T., Slagle, J.R., et al.: A triangulation method for sequential mapping of points from n-space to two-space. IEEE Transactions on Computers 27, 288–299 (1977)
Pal, N.R., et al.: Fuzzy Logic Approaches to Structure Preserving Dimensionality Reduction. IEEE Transactions on Fuzzy Systems 10(3) (June 2002)
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Rezvani, S., Prasad, G., Muir, J., McCraken, K. (2003). An Extended Multivariate Data Visualization Approach for Interactive Feature Extraction from Manufacturing Data. In: Liu, J., Cheung, Ym., Yin, H. (eds) Intelligent Data Engineering and Automated Learning. IDEAL 2003. Lecture Notes in Computer Science, vol 2690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45080-1_115
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DOI: https://doi.org/10.1007/978-3-540-45080-1_115
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