The document discusses a dataset clustering algorithm and its partitioning approach using labeled training and test data. It compares various machine learning models including k-nearest neighbors (k-nn), support vector machines (SVM), random forests (RF), multilayer perceptrons (MLP), and logistic regression (LR) in terms of their performance metrics such as AUC. This highlights the effectiveness of different algorithms in the training process.

1. Dataset
Clustering
Algorithm
Partition
with Labels
Training | Test
Training | Test
Training | Test
Training
Process
k-NN
SVM
RF
MLP
LR
AUC