  Since the label collecting is prohibitive and time-consuming, unsupervised
methods are preferred in applications such as fraud detection. Meanwhile, such
applications usually require modeling the intrinsic clusters in
high-dimensional data, which usually displays heterogeneous statistical
patterns as the patterns of different clusters may appear in different
dimensions. Existing methods propose to model the data clusters on selected
dimensions, yet globally omitting any dimension may damage the pattern of
certain clusters. To address the above issues, we propose a novel unsupervised
generative framework called FIRD, which utilizes adversarial distributions to
fit and disentangle the heterogeneous statistical patterns. When applying to
discrete spaces, FIRD effectively distinguishes the synchronized fraudsters
from normal users. Besides, FIRD also provides superior performance on anomaly
detection datasets compared with SOTA anomaly detection methods (over 5%
average AUC improvement). The significant experiment results on various
datasets verify that the proposed method can better model the heterogeneous
statistical patterns in high-dimensional data and benefit downstream
applications.
