Anomaly Detection is an unsupervised learning task aimed at detecting anomalous
behaviors with respect to historical data. In particular, multivariate Anomaly Detection has an …

In this paper, a novel, parameter-free, incremental local density and cluster-based outlier
factor (iLDCBOF) method is presented that unifies incremental versions of local outlier factor …

The problem of finding anomalies and outliers in datasets is one of the most important
challenges of modern data analysis. Among the commonly dedicated tools to solve this task …

Detecting anomalies in complex data is crucial for knowledge discovery and data mining
across a wide range of applications. While density-based methods are effective for handling …

Outlier detection is one of the most important issues in contemporary data analysis. At
present, many methods are employed for anomaly and outlier detection, but there is still no …

Anomaly detection is one of the most important problems of modern data science due to the
threat to the security of information systems as well as their users. This applies in particular …

Fuzzy systems have shown a powerful ability in multi-view learning. However, most existing
multi-view fuzzy systems require a large amount of labeled and complete multi-view data …

Data acquisition devices and environmental factors may lead to abnormal data and
misjudgment of mechanical health status in mechanical work. A new method for abnormal …

Time series anomaly detection (TSAD) is becoming increasingly vital due to the rapid growth
of time series data across various sectors. Anomalies in web service data, for example, can …

Abstract Information granules describe available experimental evidence at a more abstract
level and facilitate the concise characterization of the structure of the numeric data. The …