Data mining using PLS-trees and other projection methods

The amount of data measured during a typical manufacturing process is immense. To efficiently utilize these data without becoming overwhelmed with confusing and often conflicting information is difficult to impossible when using traditional univariate methods. Multivariate data mining methods can be used to examine large data sets by extracting relationships between variables to highlight variable correlations and deviations. Specifically, PLS-trees can be used to quickly identify significant clusters in large datasets and to highlight the differences within the groups.