  The credit cards' fraud transactions detection is the important problem in
machine learning field. To detect the credit cards's fraud transactions help
reduce the significant loss of the credit cards' holders and the banks. To
detect the credit cards' fraud transactions, data scientists normally employ
the unsupervised learning techniques and supervised learning techniques. In
this paper, we employ the graph p-Laplacian based semi-supervised learning
methods combined with the undersampling techniques such as Cluster Centroids to
solve the credit cards' fraud transactions detection problem. Experimental
results show that the graph p-Laplacian semi-supervised learning methods
outperform the current state of the art graph Laplacian based semi-supervised
learning method (p=2).
