CAREER: A General Framework for Methodical and Interpretable Anomaly Mining

异常挖掘是在数据中发现异常的任务。它在很多领域都有应用，比如安全、金融、天文和医学。然而，尽管它非常受欢迎，但对于许多现实世界的应用程序来说，它仍然是一项极具挑战性的任务。对于许多从业者来说，由于现有的定义和解决方案往往过于简单，并且不能直接符合现代应用程序的需求，因此任务定义不明确，也没有具体说明。该项目采取了必要的步骤来弥合研究和实践之间的差距，极大地提高了异常挖掘技术的可用性、有效性和可解释性，并最终使该领域成熟为更有价值的贡献者。它有望对许多具体问题产生重大影响，如内部人威胁、逃税和医疗欺诈检测，这些问题对政府、行业和社会都很重要。与行业和医院合作伙伴的合作旨在将创新引导到已部署的技术中，对安全和医疗保健产生切实影响。实现这些目标的主要议程包括开发一个新的异常挖掘框架，该框架以确证的方式利用多个异类数据源和技术，从根本上重新构建我们对定义、检测和描述真实世界异常的理解和能力。该项目正式确定了融合多个数据源的复杂异常的新定义，并发明了复杂异常检测算法，进一步提供了为检测到的异常提供理由的描述。研究还探索并模拟了系统地利用来自多种探测技术的证据的异常集合。最终，这个项目努力通过寻求有原则的基础和实践来推动异常挖掘作为一个领域的边界。

Anomaly mining is the task of finding irregularities in the data. It finds applications in a plethora of domains, such as security, finance, astronomy, and medicine. Despite its immense popularity, however, it remains an extremely challenging task for many real world applications. For many practitioners, the task is poorly defined and under-specified as existing definitions and solutions are often too simplistic and do not directly correspond to the needs of modern applications. This project takes the essential steps to bridge the gap between research and practice to dramatically improve the usability, effectiveness, and interpretability of anomaly mining techniques, and to ultimately mature the field into a more valuable contributor to the larger world. It promises significant impact on many concrete problems, such as insider threat, tax evasion, and health-care fraud detection, important for the government, industry, and the society. Collaborations with industry and hospital partners aim to shepherd innovations into deployed technology, with tangible impact on security and healthcare.The primary agenda to achieve these goals involves developing a new framework for anomaly mining that utilizes multiple heterogeneous data sources and techniques in a corroborative fashion to fundamentally reframe our understanding and ability to define, detect, and describe real-world anomalies. The project formalizes novel definitions of complex anomalies that fuse multiple data sources, and invents complex anomaly detection algorithms that further present descriptions that provide rationale for the detected anomalies. Research also explores and models anomaly ensembles that systematically harness evidences from multiple detection techniques. Ultimately, this project strives to push the boundaries of anomaly mining as a field through this quest for principled foundations and practices.