III: Large: Discovering Complex Anomalous Patterns

许多可以从数据中获得的最有趣和最有价值的发现不是来自对单个记录的评估，而是来自以某种有趣的方式识别一组异常记录。例如，它们合在一起可能预示着疾病暴发或新的犯罪活动模式的出现。人们可以将模式发现视为数据分析算法和拥有该领域专业知识的人类用户之间的交互过程。这项研究将开发一个概率方法的集成框架，以便在检测、描述、解释和学习记录组上的异常模式时与用户交互。重点是在许多情况下，数据(以及要发现的概率模式)不适合使用其他现有技术，如图挖掘或频繁集。建议的方法将搜索记录的任意子集，并评估它们与已知的、可能非常复杂的概率模式的对应关系，或者它们在各种学习的统计模型下与基线数据的匹配失败。这些方法将帮助用户理解和模拟已发现的、以前未知的异常，以便在将来遇到时能够识别为已知模式。智力价值这个协作的研究团队将开发、实现和评估一个通用的、全面的、广泛适用的模式发现概率框架。拟议的工作将解决这些具有挑战性和重要的研究问题：-如何将机器学习概念，如Classi&amp；#64257；阳离子和异常检测被推广到考虑记录组而不是单个记录？-检测算法如何同时检测和区分已知和当前未知的模式类型？-算法如何向用户清楚地解释发现了什么模式以及为什么？-算法如何通过来自用户的反馈来学习新的模式类型？从海量数据集中的记录组中检测、表征、解释和学习模式的能力将为推进从数据中发现知识提供一种定性的新方法。更广泛的影响尽管这些算法的应用程序数不胜数，但开发和测试将优先用于重症监护病房(ICU)和飞机维修中的患者护理领域。通过该团队现有的合作，这些算法还将在项目期间用于其他领域，包括食品安全、天文天空测量中的科学发现以及犯罪活动的地理热点探测。总而言之，这些应用程序将在广泛的领域和任务中展示方法的价值。关键词：异常模式；模式发现；概率模型；增量学习。

Many of the most interesting and valuable discoveries that can be made from data arise not from the evaluation of single records, but from identifying a set of records that are anomalous in some interesting way. Together they may indicate for example the emergence of a disease outbreak or new patterns of criminal activity. One can view pattern discovery as an interactive process between data analysis algorithms and human users who have expertise in the domain. This research will develop an integrated framework of probabilistic methods to interact with the user in detecting, characterizing, explaining, and learning anomalous patterns over groups of records. The focus is on the many situations where the data (and the probabilistic patterns to be discovered) are not appropriate for using other existing techniques, such as graph mining or frequent sets. The proposed methods will search over arbitrary subsets of records and evaluate their correspondence to known, potentially very complex, probabilistic patterns, or their failure to match baseline data under various learned statistical models. These methods will assist the user in understanding and modeling the discovered, previously unknown anomalies to be identifiable as a known pattern when encountered in the future. Intellectual MeritThis collaborative team of researchers will develop, implement, and evaluate a general, comprehensive, and widely applicable probabilistic framework for pattern discovery. The proposed work will address these challenging and important research questions: - How can machine learning concepts such as classification and anomaly detection be generalized to consider groups of records rather than single records? - How can a detection algorithm simultaneously detect and differentiate between known and currently unknown pattern types? - How can an algorithm explain clearly to a user what pattern was found and why? - How can an algorithm learn new pattern types through feedback from a user?The ability to detect, characterize, explain, and learn patterns from groups of records in massive datasets will provide a qualitatively new approach for advancing discovery of knowledge from data. Broader ImpactAlthough the applications for these algorithms are innumerable, development and testing will be prioritized in the areas of patient care in the intensive care unit (ICU) and aircraft fleet maintenance. Through the team's existing collaborations, the algorithms will also be used during the project in other areas including food safety, scientific discovery in astronomy sky surveys, and detection of geographic hot-spots of criminal activity. Together, these applications will demonstrate the methods' value across a wide spectrum of domains and tasks. Key Words: anomalous patterns; pattern discovery; probabilistic models; incremental learning.