%0 Conference Paper %1 sac08:eduardo %A Spinosa, E. %A Gama, J. %A Carvalho, A. %B Proceedings of the 2008 ACM Symposium on Applied computing %D 2008 %I ACM Press %K imported %P to appear %T Cluster-based novel concept detection in data streams applied to intrusion detection in computer networks %X In this paper, a cluster-based novelty detection technique capable of dealing with a large amount of data is presented and evaluated. Starting with examples of a single class that describe the normal profile, the proposed technique is able to detect novel concepts initially as cohesive clusters of examples and later as sets of clusters in an unsupervised incremental learning fashion. Experimental results with the KDD Cup 1999 data set show that the technique is capable of dealing with data streams, successfully learning novel concepts that are pure in terms of the real class structure.

@inproceedings{sac08:eduardo, abstract = {In this paper, a cluster-based novelty detection technique capable of dealing with a large amount of data is presented and evaluated. Starting with examples of a single class that describe the normal profile, the proposed technique is able to detect novel concepts initially as cohesive clusters of examples and later as sets of clusters in an unsupervised incremental learning fashion. Experimental results with the KDD Cup 1999 data set show that the technique is capable of dealing with data streams, successfully learning novel concepts that are pure in terms of the real class structure.}, added-at = {2008-04-30T12:59:47.000+0200}, author = {Spinosa, E. and Gama, J. and Carvalho, A.}, biburl = {https://www.bibsonomy.org/bibtex/21f09a9632c95d80d1ef17732c299023e/kdubiq}, booktitle = {Proceedings of the 2008 ACM Symposium on Applied computing}, description = {KDubiq Blueprint}, interhash = {5aa8cd696204dceb229b7331d99d55f1}, intrahash = {1f09a9632c95d80d1ef17732c299023e}, keywords = {imported}, pages = {to appear}, publisher = {ACM Press}, timestamp = {2008-04-30T13:00:53.000+0200}, title = {Cluster-based novel concept detection in data streams applied to intrusion detection in computer networks}, year = 2008 }