It often reveals relationships that were previously unsuspected, thereby allowing interpretations of the data that may not ordinarily result from examination of the data. PLS can be used to discover important features of a large data set. PLS is most commonly used for constructing predictive model when the information contained in a large number of original variables and they are highly collinear. There are two primary reasons for using PLS: Partial Least Squares regression (PLS) is used for constructing predictive models when there are many highly collinear factors. It is faster than Hierarchical but need user know the centroid of the observations, or at least the number of groups to be clustered.Дiscriminant analysis is used to distinguish distinct sets of observations, and to allocate new observations to previously defined groups. Use K-means clustering to classify observations through K number of clusters. In this method, elements are grouped into successively larger clusters by some measures of similarity or distance.

This form of analysis is an effective way to discover relationships within a large number of variables or observations.

PCA is thus often used as a technique for reducing dimensionality.Д¬luster analysis is used to construct smaller groups with similar properties from a large set of heterogeneous data. Principal Component Analysis (PCA) is used to explain the variance-covariance structure of a set of variables through linear combinations of those variables.

Split plot design in xlstat