Unsupervised learning: Local and global structure preservation in industrial data

Abstract

This article examines the use of data mining and machine learning algorithms for knowledge discovery in industrial processes, with a focus on understanding how these methods distinguish and represent the global and local structures underlying data. The effectiveness of alternative dimensionality reduction algorithms in characterizing process operations is also explored, with an emphasis on a novel dimensionality reduction method, PaCMAP, compared to the dependable historical method of PCA and the more recent t-SNE. The article demonstrates a visualization method for identifying the variables responsible for the separation between selected clusters and studies the effects of key elements of the algorithms on cluster analysis in light of domain knowledge. Results from an industrial case study of a pyrolysis reactor validates the applicability of these methods in real-life scenarios. Overall, this article provides insights into the use of machine learning and dimensionality reduction algorithms for knowledge discovery in industrial processes.