Multidimensional Visualization and Clustering of Historical Process Data

Multivariate statistical analysis using principal components can reveal patterns and structures within a data set and give insights into process performance and operation. The output medium is usually a two-dimensional screen, however, so it is a challenge to visualize the multidimensional structure of a data set by means of a two-dimensional plot. An automated method of visualization is described in the form of a hierarchical classification tree that can be used to view and report on the structure within a multivariate principal component model of three or more dimensions. The tree is generated from an unsupervised agglomerative hierarchical clustering algorithm which operates in the score space of the principal component model, and a recursive algorithm is used to draw the tree. It is readily adaptable to a wide range of multivariate analysis applications including process performance analysis and process or equipment auditing. Its application are illustrated with industrial data sets.