Abstract
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.
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Ceci, M., Appice, A., Loglisci, C., Caruso, C., Fumarola, F., Malerba, D. (2009). Novelty Detection from Evolving Complex Data Streams with Time Windows. In: Rauch, J., Raś, Z.W., Berka, P., Elomaa, T. (eds) Foundations of Intelligent Systems. ISMIS 2009. Lecture Notes in Computer Science(), vol 5722. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04125-9_59
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DOI: https://doi.org/10.1007/978-3-642-04125-9_59
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