Abstract—Unsupervised 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.
Index Terms—ART1, Dendograms, K-means clustering, Wine, PCA, SQC.
Naga C. Kavuri, M.Tech student:, e-mail: biochaitanya@gmail.com
Madhusree Kundu, Associate Professor, Department of Chemical Engineering, National Institute of Technology, Rourkela, India. Corresponding Author,. E-mail:mkundu@nitrkl.ac.in; madhushreek@yahoo.com. Fax: 0661-2462999
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Cite: N. C. Kavuri and Madhusree Kundu, "ART1 Network: Application in Wine Classification," International Journal of Chemical Engineering and Applications vol. 2, no. 3, pp. 189-195, 2011.
