Over the past decade, we have witnessed an enormous amount of research effort dedicated to the design of efficient outlier detection techniques while taking into consideration efficiency, accuracy, high-dimensional data, and distributed environments, among other factors. In this article, we present and examine these characteristics, current solutions, as well as open challenges and future research directions in identifying new outlier detection strategies. We propose a taxonomy of the recently designed outlier detection strategies while underlying their fundamental characteristics and properties. We also introduce several newly trending outlier detection methods designed for high-dimensional data, data streams, big data, and minimally labeled data. Last, we review their advantages and limitations and then discuss future and new challenging issues.

中文翻译：

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异常值检测

在过去的十年中，我们目睹了大量致力于设计高效异常值检测技术的研究工作，同时考虑了效率、准确性、高维数据和分布式环境等因素。在本文中，我们介绍并研究了这些特征、当前解决方案以及在识别新异常值检测策略方面的开放挑战和未来研究方向。我们提出了最近设计的异常值检测策略的分类，同时基于它们的基本特征和属性。我们还介绍了几种新的趋势异常值检测方法，专为高维数据、数据流、大数据和最小标记数据而设计。最后，我们回顾了它们的优势和局限性，然后讨论未来和新的具有挑战性的问题。