Morphological classification of the rat lateral cerebellar nuclear neurons by principal component analysis

2002 ◽  
Vol 455 (2) ◽  
pp. 139-155 ◽  
Author(s):  
Fahad Sultan ◽  
Uwe Czubayko ◽  
Peter Thier
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Morphological Classification

Principal Component Analysis of X-ray Diffraction Patterns To Yield Morphological Classification of Brucite Particles

2007 ◽  
Vol 79 (5) ◽  
pp. 2091-2095 ◽  
Author(s):  
Charlene R. S. Matos ◽  
Maria José Xavier ◽  
Ledjane S. Barreto ◽  
Nivan B. Costa ◽  
Iara F. Gimenez
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
X Ray Diffraction ◽  
Morphological Classification ◽  
X Ray ◽  
Diffraction Patterns

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

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