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Effective Decision Making and Data Visualization Using Partitive Clustering and Principal Component Analysis (PCA) for High Dimensional Pareto Frontier Data

Journal Article
2015-01-0460
ISSN: 1946-3979, e-ISSN: 1946-3987
DOI: https://doi.org/10.4271/2015-01-0460
Published April 14, 2015 by SAE International in United States
Effective Decision Making and Data Visualization Using Partitive Clustering and Principal Component Analysis (PCA) for High Dimensional Pareto Frontier Data
Sector:
Citation: Kansara, S., Parashar, S., and Xue, Z., "Effective Decision Making and Data Visualization Using Partitive Clustering and Principal Component Analysis (PCA) for High Dimensional Pareto Frontier Data," SAE Int. J. Mater. Manf. 8(2):336-343, 2015, https://doi.org/10.4271/2015-01-0460.
Language: English

References

  1. Parashar , Sumeet , Pediroda Valentino , and Poloni Carlo Self organizing maps (SOM) for design selection in robust multi-objective design of aerofoil 46th AIAA Aerospace Sciences Meeting and Exhibit 2008 914 2008
  2. Berguin , Steven H. , and Mavris Dimitri N. Dimensionality Reduction In Aerodynamic Design Using Principal Component Analysis With Gradient Information 2014
  3. Jolliffe I.T. Principal Component Analysis, Series: Springer Series in Statistics 2nd Springer NY 2002 XXIX 487 978-0-387-95442-4
  4. Clarich , A. , Geremia , P. , Parashar , S. , Russo , R. Use of Multi-Variate Data Analysis Techniques in modeFRONTIER for Efficient Optimization and Decision Making Proceedings of the 13th AIAA/ISSMO Multidisciplinary Analysis Optimization Conference Fort Worth, TX 2010
  5. Obayashi , S. , Sasaki , D. Visualization and Data Mining of Pareto Solutions Using Self Organizing Map Evolutionary Multi-Criterion Optimization Lecture Note in Computer Science Springer 2632 200

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