Novelty Detection from Evolving Complex Data Streams with Time Windows

Novelty detection in data stream mining denotes the identification of new or unknown situations in a stream of data elements flowing continuously in at rapid rate. This work is a first attempt of investigating the anomaly detection task in the (multi-)relational data mining. By defining a data block as the collection of complex data which periodically flow in the stream, a relational pattern base is incrementally maintained each time a new data block flows in. For each pattern, the time consecutive support values collected over the data blocks of a time window are clustered, clusters are then used to identify the novelty patterns which describe a change in the evolving pattern base. An application to the problem of detecting novelties in an Internet packet stream is discussed.