Abstract
The use of High Volume Instrument (HVI) to measure cotton lint characteristics produces high dimensional data. A model which utilized Kohonen Self Organizing Maps (SOM) to visualize cotton lint HVI data, k-means clustering technique to cluster the data and Probabilistic Neural Network (PNN) for data classification was designed and tested using Kenyan cotton lint. According to the model the Kenyan cotton lint can be grouped into four clusters, which were successfully classified by using PNN with a correlation coefficient (R-value) of 1.
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Mwasiagi, J.I., Wang, X.H. & Huang, X.B. The use of k-means and artificial neural network to classify cotton lint. Fibers Polym 10, 379–383 (2009). https://doi.org/10.1007/s12221-009-0379-z
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DOI: https://doi.org/10.1007/s12221-009-0379-z