Classification of Aircraft Design Variables using Principal Component Analysis in Aircraft Conceptual Design

2017 ◽  
Vol 16 (0) ◽  
pp. 65-71 ◽  
Author(s):  
Ryohei OMIYA ◽  
Kenichi RINOIE
Keyword(s):  
Principal Component Analysis ◽  
Conceptual Design ◽  
Principal Component ◽  
Aircraft Design ◽  
Component Analysis ◽  
Design Variables ◽  
Aircraft Conceptual Design

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.

Principal Component Analysis and CNN for Classification of Power Quality Disturbances

2021 ◽  
Author(s):  
Ananta Agarwalla ◽  
Diya Dileep ◽  
P. Jyothsana ◽  
Purnima Unnikrishnan ◽  
Karthik Thirumala
Keyword(s):  
Principal Component Analysis ◽  
Power Quality ◽  
Principal Component ◽  
Component Analysis ◽  
Power Quality Disturbances
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