DETECTION OF PLANT-WIDE DISTURBANCES USING A SPECTRAL CLASSIFICATION TREE

Abstract This article demonstrates the use of agglomerative hierarchical clustering to detect the structure within a data set. When combined with spectral principal component analysis to capture the main spectral features of a data set it allows visualization of the structure of a model with an optimum number of principal components. The paper presents the theory and methods for construction of the tree and gives an example using industrial data.

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