Computational exploratory data analysis in cotton spinning

There are a vast number of useful analyses that can be solved by discriminant analysis. The main advantage these methods have is that they are generally easier to apply to blending cotton analysis than quantitative methods since they do not need any primary approximation data to build the model. They give simple “pass” or “fail” answers as to how well the samples match by comparing them to training sets of the desired quality samples. They learn to recognize the cotton blends based entirely on the cotton data itself without any other external information other than the analyst’s logical grouping of the cotton into training sets. Discriminant analysis is the statistical technique that is most commonly used to solve complex problems. Its use is appropriate when we can classify data into two or more groups, and when we want to find one or more functions of quantitative measurements that can help discriminate among the known groups. The goal of this analysis is to present a method for predicting which group a new case is most likely to fall into, or to obtain a small number of useful predictor variables. In this paper, the intention is to classify cotton bales into well define groups or categories based on a training set of similar samples for grading cotton blends. A new algorithm has been implemented using discriminant analysis and its advantages in quality design of cotton blends are verified.