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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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
Publisher Name: Springer, Berlin, Heidelberg
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