Many real‐world data mining applications have to deal with unlabeled streaming data. They
are unlabeled because the sheer volume of the stream makes it impractical to label a …

Big Data is identified by its three Vs, namely velocity, volume, and variety. The area of data
stream processing has long dealt with the former two Vs velocity and volume. Over a decade …

The spin structure of domain walls in constrictions down to 30 nm is investigated both
experimentally with electron holography and with simulations using a Heisenberg model …

We propose a concept drift detection method utilizing statistical change detection in which a
drift detection method and the Page-Hinkley test are employed. Our method enables users …

Due to the presence of data streams in many applications like banking, sensor networks,
and telecommunication, data stream mining has gained increased attention. Data stream is …

Recently advancement in hardware and software has enabled processing of large amount
of data efficiently. Many applications generate big data rapidly in high fluctuating rates. The …

Real-world data stream classification often deals with multiple types of concept drift,
categorized by change characteristics such as speed, distribution, and severity. When labels …

Change detection is the process of identifying differences in the state of an object or
phenomenon by observing it at different times or different locations in space. In the …

Trois expérimentations ont été étudiées: la détection d'usurpation d'identité, le profilage de
comportement de blanchiment d'argent et enfin la prédiction de toute forme d'activité …

The research work presented in this thesis concerns the development of unsupervised
learning approaches adapted to large relational and dynamic data-sets. The combination of …