Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/1484
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dc.contributor.authorKavuri, N C-
dc.contributor.authorKundu, M-
dc.date.accessioned2011-07-12T06:16:05Z-
dc.date.available2011-07-12T06:16:05Z-
dc.date.issued2011-06-
dc.identifier.citationInternational Journal of Chemical Engineering and Applications, Vol. 2 , No. 3 , June 2011en
dc.identifier.urihttp://hdl.handle.net/2080/1484-
dc.descriptionCopyright for this article belongs to International Journal of Chemical Engineering and Applications (IJCEA)en
dc.description.abstractUnsupervised neural network (NN) based on Adaptive Resonance Theory (ART1) was successfully implemented as an alternative to statistical classifier in order to discriminate among the 178 samples of wine possessing 13 numbers of feature variables. A pattern recognition tool, principal component analysis (PCA) was applied to reduce the dimensionality of the feature variables by 5; out of which the first 2 numbers of principal components captured over 55.4 % of the variance of the dataset of wine. Supervised nonhierarchical K-means clustering was used to designate the classes available among the wine samples, hence discrimination. Supervised hierarchical clustering technique was also applied for discrimination with a mention of their classification level in the produced dendograms. After the discrimination made by hierarchical as well as non- hierarchical clustering, the ART1 classifier was designed.en
dc.format.extent746828 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoen-
dc.publisherInternational Journal of Chemical Engineering and Applications (IJCEA)en
dc.subjectART1en
dc.subjectDendogramsen
dc.subjectK-means clusteringen
dc.subjectWineen
dc.subjectPCAen
dc.subjectSQCen
dc.titleART1 Network: Application in Wine Classificationen
dc.typeArticleen
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