Novel methods for matrix factorization introduce constraints to the decomposed matrices, allowing for unique kinds of analysis. One significant modification is the binary matrix factorization for binary matrices. This technique can reveal common subsets and mixing of subsets, making it useful in a variety of applications, such as market basket analysis, topic modeling, and recommendation systems. Despite the advantages, current approaches face a trade-off between accuracy, scalability, and explainability. While gradient descent-based methods are scalable, they yield high reconstruction errors when thresholded for binary matrices. Conversely, heuristic methods are not scalable. To overcome this, this thesis propose a post-processing procedure for discretizing matrices obtained by gradient descent. This novel approach recovers the reconstruction error post-thresholding and successfully processes larger matrices within a reasonable timeframe. We apply this technique to many applications including a novel pipeline for discovering and visualizing patterns in petrochemical batch processes.