CAREER: A General Framework for Methodical and Interpretable Anomaly Mining

异常挖掘是发现数据中的不规则性的任务。它在安全、金融、天文学和医学等众多领域都有应用。尽管它非常受欢迎，但是，对于许多真实的世界应用来说，它仍然是一项极具挑战性的任务。对于许多从业者来说，这项任务定义得很差，并且由于现有的定义和解决方案往往过于简单，并且不直接对应于现代应用程序的需求，因此规定不足。该项目采取了必要的步骤来弥合研究与实践之间的差距，以显着提高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大的世界做出更有价值的贡献。它有望对许多具体问题产生重大影响，如内部威胁，逃税和医疗欺诈检测，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部署的技术中，对安全和医疗保健产生切实的影响。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构数据源和技术，从根本上重新构建我们定义，检测和描述真实世界异常的理解和能力。该项目形式化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论依据的描述。研究还探索和建模异常集合，系统地利用多种检测技术的证据。最终，该项目致力于通过对原则性基础和实践的探索来推动异常采矿作为一个领域的界限。

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.