Clustering and Classification of Cotton Lint Using Principle Component Analysis, Agglomerative Hierarchical Clustering, and K-Means Clustering
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Date
2017
Journal Title
Journal ISSN
Volume Title
Publisher
Journal of Natural Fibers
Abstract
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 analysis, cotton yellowness and short fiber index were found
to account for the highest variability. At 5% significance level, the highest
correlation (0.73) was found between short fiber index and yellowness.
Based on Cotton Outlook’s world classification and USDA Standards, the
cotton under test was deemed of high and uniform quality, falling between
Middling and Good Middling grades. Our suggested classification integrates
all lint quality parameters, unlike the traditional methods that consider
selected parameters.
Description
Keywords
Agglomerative hierarchical clustering (AHC), Classification, Cotton quality, High volume instrument (HVI), k-means clustering, Principal component analysis (PCA)
Citation
Edwin Kamalha , Jovan Kiberu, Ildephonse Nibikora, Josphat Igadwa Mwasiagi & Edison Omollo (2017): Clustering and Classification of Cotton Lint Using Principle Component Analysis, Agglomerative Hierarchical Clustering, and K-Means Clustering, Journal of Natural Fibers, DOI: 10.1080/15440478.2017.1340220