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ISMIS
2009
Springer

Novelty Detection from Evolving Complex Data Streams with Time Windows

9 years 22 days ago
Novelty Detection from Evolving Complex Data Streams with Time Windows
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.
Michelangelo Ceci, Annalisa Appice, Corrado Loglis
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where ISMIS
Authors Michelangelo Ceci, Annalisa Appice, Corrado Loglisci, Costantina Caruso, Fabio Fumarola, Donato Malerba
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