Many well-established anomaly detection methods use the distance of a sample to those in its local neighbourhood: so-called `local outlier methods', such as LOF and DBSCAN. They are popular for their simple principles strong performance on unstructured, feature-based data that is commonplace many practical applications. However, they cannot learn adapt particular set due lack trainable paramete...

Efficient outlier detection in a large-sized big data environment incurs much of complexity in processing the information and to handle it in a proficient way. For segregating outliers from those normal data items, many of the prevailing methodologies experiences complexity in accordance with the features involved in every single attribute. On recognizing appropriate features associated the cha...

Dynamic graphs are a powerful way to model an evolving set of objects and their ongoing interactions. A broad spectrum of systems, such as information, communication, and social, are naturally represented by dynamic graphs. Outlier (or anomaly) detection in dynamic graphs can provide unique insights into the relationships of objects and identify novel or emerging relationships. To date, outlier...

We study a novel outlier detection problem that aims to identify abnormal input-output associations in data, whose instances consist of multi-dimensional input (context) and output (responses) pairs. We present our approach that works by analyzing data in the conditional (input–output) relation space, captured by a decomposable probabilistic model. Experimental results demonstrate the ability o...

In this work, we deal with a robust fitting of wrapped normal model to multivariate circular data. Robust estimation is supposed mitigate the adverse effects outliers on inference. Furthermore, use proper method leads definition effective outlier detection rules. achieved by suitable modification classification-expectation-maximization algorithm that has been developed perform maximum likelihoo...

Automated detection of anomalous trajectories is an important problem in the surveillance domain. Various algorithms based on learning of normal trajectory patterns have been proposed for this problem. Yet, these algorithms suffer from one or more of the following limitations: First, they are essentially designed for offline anomaly detection in databases. Second, they are insensitive to local ...