Outlier detection is a statistical procedure that aims to find suspicious events or items that
are different from the normal form of a dataset. It has drawn considerable interest in the field …

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 …

Detecting anomalous subsequences in time series data is an important task in areas
ranging from manufacturing processes over finance applications to health care monitoring …

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 rapid evolution of technology has led to the generation of high dimensional data
streams in a wide range of fields, such as genomics, signal processing, and finance. The …

Existing anomaly detection models for time series are primarily trained with normal-point-
dominant data and would become ineffective when anomalous points intensively occur in …

Given a stream of entries in a multi-aspect data setting ie, entries having multiple
dimensions, how can we detect anomalous activities in an unsupervised manner? For …

The accurate estimation of remaining useful life (RUL) is significant for the operation,
maintenance, and avoidance of unplanned downtime of rotating machinery. To improve the …

For identifying credit card risk in massive and high dimensionality data, feature selection is
considered very important to improve classification performance and fraud identification …

Many research areas depend on group anomaly detection. The use of group anomaly
detection can maintain and provide security and privacy to the data involved. This research …