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In this paper, a delineation of homogeneous precipitation regions was made based on the multivariate methods of principal component analysis and hierarchical ascending clustering.
Based on this, using the methods of principal component analysis (short for PCA) and kernel principal component analysis (short for KPCA) to extract the feature from the fault features of shortlisted 16-dimensional data feature, then the effect of reducing dimension analysis are compared.
The unsupervised methods of principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to explore clustering of similar spectra using Unscrambler software [24], [25].
Methods of principal component analysis (PCA) were used for descriptive evaluation of the experimental data.
In order to identify possible outliers, two different methods of principal component analysis (PCA) mapping and standard scores were used.
The differences in composition between patterns with the same denomination found in the literature, and the different denominations of factors of similar composition may be explained by the subjectivity inherent to the methods of principal component analysis [ 25].
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Tongzon [17] suggested the method of principal component analysis for the classification of ports.
In this way, we use the traditional method of principal components (PC) and additionally, partial least squares (PLS).
The method of principal components or component analysis is based upon the early work of Pearson with specific adaption to principal component analysis suggested by the Hotelling (1933).
Underlying constructs for Likert measures were extracted through exploratory factor analysis via SPSS 21.0 using the method of principal components with promax rotation.
Employing virtual modelling techniques and the method of Principal Component Analysis, bicycle affordance for selected representative cases and various bicycle sizes has been examined.
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