… Anomaly mining is an exciting research area that has extensive use in a wide variety of
application domains. An overview of outlier detection techniques in time-series data has been …

… Specifically, we first present recent advances in anomaly detection, discussing the pros and
… process of anomaly mining into two steps, that is, subspace search and anomaly ranking. …

… dataset by always predicting/determining that the system behaves normally. Therefore, to
more precisely evaluate the performance of the anomaly detection/prediction mechanisms by …

… anomaly detection approaches, by empirically studying the performance of 29 semi-supervised
anomaly … -of-the-art techniques on diverse anomaly detection problems. We also observe …

… data mining studies based on anomaly detection. Further it discusses various methods and
architectures of anomaly detection based on statistical, machine learning and data mining …

… current research trends are discussed to widen the applications and effectiveness of the
anomaly detection technology before deriving future … current technological progress in anomaly …

… [12] focuses indeed on data-mining anomaly detection techniques specifically applied to …
the present survey is to complete the previous contributions by proposing a review of anomaly …

The construction industry is experiencing remarkable growth in the data generation. Data
mining (DM) from considerable amount of data in the construction industry has emerged as an …

Anomaly detection, aka outlier detection or novelty … anomaly detection, ie, deep anomaly
detection, has emerged as a critical direction. This article surveys the research of deep anomaly …

… anomaly detection task. Our study also shows the current state-ofthe-art in time series anomaly
… We indicate potential for future research and, in this way, support the development of new …