Clustering and Classification of Cotton Lint Using Principle Component Analysis, Agglomerative Hierarchical Clustering, and K-Means Clustering

Clustering and Classification of Cotton Lint Using Principle Component Analysis, Agglomerative Hierarchical Clustering, and K-Means Clustering

Cotton from the three cotton growing regions of Uganda was characterized for 13 quality parameters using the High Volume Instrument (HVI). Principal Component Analysis (PCA), Agglomerative Hierarchical Clustering (AHC) and k-means clustering were used to model cotton quality parameters. Using factor...

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Bibliographic Details
Main Authors: Edwin Kamalha, Jovan Kiberu, Ildephonse Nibikora, Josphat Igadwa Mwasiagi, Edison Omollo
Format: Article
Language:English
Published: Taylor & Francis Group 2018-05-01
Series:Journal of Natural Fibers
Subjects:
agglomerative hierarchical clustering (ahc)
classification
cotton quality
high volume instrument (hvi)
k-means clustering
principal component analysis (pca)
Online Access:http://dx.doi.org/10.1080/15440478.2017.1340220

Internet

http://dx.doi.org/10.1080/15440478.2017.1340220

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