Outlier detection is a key data mining task for identifying abnormal objects from massive data. Due to the high expense of acquiring ground truth, practitioners lean towards building a large number of unsupervised models for further combination and analysis, rather than relying on a single model out of reliability consideration. However, this poses scalability challenge to high-dimensional, large datasets. In this study, we propose a three-module framework called SUOD to address the challenge. It can accelerate outlier model building and scoring when a large number of base models are used. It focuses on three complementary levels to speed up the process while controlling prediction performance degradation at the same time. At the data level, its Random Projection module projects high-dimensional data onto diversified low-dimensional subspaces while preserving the pairwise distance relationship. At the model level, SUOD's Pseudo-supervised Approximation module can approximate and replace fitted unsupervised models by low-cost supervised regressors, leading to fast offline scoring on new-coming samples with better interpretability. At the system level, Balanced Parallel Scheduling module mitigates the workload imbalance within distributed systems, which is helpful for heterogeneous outlier ensembles. As the three modules are independent with different focuses, they have great flexibility to "mix and match". The framework is also designed with extensibility in mind. One may customize each module based on specific use cases, and the framework may be generalized to other learning tasks as well. Extensive experiments on more than 20 benchmark datasets demonstrate SUOD's effectiveness. In addition, a real-world deployment system on fraudulent claim analysis by IQVIA is also discussed. The full framework, documentation, and examples are openly shared at https://github.com/yzhao062/SUOD.

One of the basics steps when it comes to the analysis of the data is outlier detection, ie. During this step of data analysis abnormalities in the dataset are detected. This process is often driven by some kind of unsupervised model, that will eventually acquire ground truth. However, these models are unstable. This means that using just one unsupervised model is risky.

Outlier detection is a key field of machine learning for identifying abnormal data objects. Due to the high expense of acquiring ground truth, unsupervised models are often chosen in practice. To compensate for the unstable nature of unsupervised algorithms, practitioners from high-stakes fields like finance, health, and security, prefer to build a large number of models for further combination and analysis. However, this poses scalability challenges in high-dimensional large datasets. In this study, we propose a three-module acceleration framework called SUOD to expedite the training and prediction with a large number of unsupervised detection models. SUOD's Random Projection module can generate lower subspaces for high-dimensional datasets while reserving their distance relationship. Balanced Parallel Scheduling module can forecast the training and prediction cost of models with high confidence---so the task scheduler could assign nearly equal amount of taskload among workers for efficient parallelization. SUOD also comes with a Pseudo-supervised Approximation module, which can approximate fitted unsupervised models by lower time complexity supervised regressors for fast prediction on unseen data. It may be considered as an unsupervised model knowledge distillation process. Notably, all three modules are independent with great flexibility to "mix and match"; a combination of modules can be chosen based on use cases. Extensive experiments on more than 30 benchmark datasets have shown the efficacy of SUOD, and a comprehensive future development plan is also presented.