Figure 3: Unsupervised learning techniques (e.g., hierarchical
clustering, tree classification) can be used for clustering analysis.
This hierarchical clustering was constructed based on Euclidean distance
(between T1 relaxation and T2relaxation) and its´ quantitative linkages (e.g., inter/intra cluster
similarity) shown in a heat map. (A, A´, B, B´, C, C´, C´´, D) were the
variants of the same oil content taken from different manufacturers.
Tree classification method is shown for comparison (Supp. Fig. 2).
Using unsupervised learning, the relationship between each objects were
rapidly constructed using clustering analysis (e.g., tree
classification, hierarchical clustering) and its´ quantitative linkages
(e.g., inter/intra cluster similarity) were shown on a dendogram and a
heat map (Fig. 3). Supervised learning models (i.e., neural network,
kNN, logistic regression, naïve Bayes, and random forest) can be used to
train the datasets and the best model with the highest accuracy can be
chosen to predict the object classification (e.g., oil classification,
infection/non-infection) using pre-trained datasets (Fig. 4 and Table
1).