Data Stream Mining has become a hot topic and is concerned with the analytics of data that arrives in real-time and at a fast speed. Two general challenges in Data Stream Mining are (1) the data stream is infinite and storing the data and learning off line is not possible and (2) the pattern in the data may change over time (known as concept drift). Challenge (1) is typically met through algorithms that only need one pass through the data; and challenge (2) is typically met through frequent feedback about the pattern and thus changes of pattern encoded in the stream.
- Applications are accepted all all year round.
- Self-funded PhD students only.
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