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  4. Enhancing the interpretability of terahertz data through unsupervised classification
 
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2009
Conference Paper
Title

Enhancing the interpretability of terahertz data through unsupervised classification

Abstract
We present the applicability of hierarchical agglomerative cluster algorithms to terahertz (THz) spectroscopic analysis. We show the influence of different windowing and filtering methods in the spectral data preprocessing to enhance the clustering results. Two distance measures are compared. Classical Euclidean distance on the full frequency range and a distance working only on the minima of the spectra. We further show the adaptability of our clustering methods for THz hyper-spectral image classification and visualization.
Author(s)
Stephani, H.
Herrmann, M.
Fraunhofer-Institut für Physikalische Messtechnik IPM  
Wiesauer, K.
Katletz, S.
Heise, B.
Mainwork
Fundamental and applied metrology. Proceedings of the XIX IMEKO world congress 2009. CD-ROM  
Conference
IMEKO World Congress 2009  
Link
Link
Language
English
Fraunhofer-Institut für Physikalische Messtechnik IPM  
Keyword(s)
  • clustering

  • THz spectroscopy

  • THz imaging

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