Cellulose nanofibrils (CNFs) can be used as building blocks for future sustainable materials including strong and stiff filaments. The goal of this project is to introduce a data analysis of flow-induced birefringence experiments by means of unsupervised learning techniques. By reducing the dimensionality of the data with Principal Component Analysis (PCA) we are able to exploit information for the different cellulose materials at several concentrations and compare them to each other. The data set is comprised of large scale measurements with 100 samples, 3 runs per sample and 15 000 data frames per each test. Our approach aims at classifying the CNF materials at different concentrations by applying unsupervised machine learning algorithms, like k-means and Gaussian Mixture Models (GMMs). The focus is given to the initial relaxation of birefringence after the flow is stopped to have a better understanding of the Brownian dynamics for the given materials and concentrations.