In data stream environments, drift detection methods are used to identify when the context
has changed. This paper evaluates eight different concept drift detectors (ddm, eddm, pht …

Several algorithms for clustering data streams based on k-Means have been proposed in
the literature. However, most of them assume that the number of clusters, k, is known a priori …

In the real word, the environment is often dynamic instead of stable. Usually the underlying
data of a problem changes with time, which enhances the difficulties when learning a model …

We propose incremental least squares density difference (LSDD) change detection method,
an incremental test to detect changes in stationarity based on the difference between the …

IP networks are constantly targeted by new techniques of denial of service attacks (SYN
flooding, port scan, UDP flooding, etc), causing service disruption and considerable financial …

Real-time fault detection and isolation are important tasks in process monitoring. A real-time
contrasts (RTC) control chart converts the process monitoring problem into a real-time …

The paper proposes an effective change detection test for online monitoring data streams by
inspecting the least squares density difference (LSDD) features extracted from two non …

Abstract Knowledge discovery on biomedical data can be based on on-line, data-stream
analyses, or using retrospective, timestamped, off-line datasets. In both cases, changes in …

Anomaly detection and change analysis are challenging tasks in stream data mining. We
illustrate a novel method that addresses both these tasks in geophysical applications. The …

With the advanced technology of medical devices and sensors, an abundance of medical
data streams are available. However, data analysis techniques are very limited, especially …