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

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dc.contributor.authorKamalha, Edwin
dc.contributor.authorKiberu, Jovan
dc.contributor.authorNibikora, Ildephonse
dc.contributor.authorIgadwa Mwasiagi, Josphat
dc.contributor.authorOmollo, Edison
dc.date.accessioned2022-12-04T12:47:47Z
dc.date.available2022-12-04T12:47:47Z
dc.date.issued2017
dc.description.abstractCotton 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.en_US
dc.identifier.citationEdwin 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: 10.1080/15440478.2017.1340220en_US
dc.identifier.urihttp://dx.doi.org/10.1080/15440478.2017.1340220
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/5815
dc.language.isoenen_US
dc.publisherJournal of Natural Fibersen_US
dc.subjectAgglomerative hierarchical clustering (AHC)en_US
dc.subjectClassificationen_US
dc.subjectCotton qualityen_US
dc.subjectHigh volume instrument (HVI)en_US
dc.subjectk-means clusteringen_US
dc.subjectPrincipal component analysis (PCA)en_US
dc.titleClustering and Classification of Cotton Lint Using Principle Component Analysis, Agglomerative Hierarchical Clustering, and K-Means Clusteringen_US
dc.typeArticleen_US
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