Stream clustering algorithms are traditionally designed to process streams efficiently and to adapt to the evolution of the underlying population. This is done without assuming any prior knowledge about the data. However, in many cases, a certain amount of domain or background knowledge is available, and instead of simply using it for the external validation of the clustering results, this knowledge can be used to guide the clustering process. In non-stream data, domain knowledge is exploited in the context of
%0 Book Section
%1 noKey
%A Ruiz, Carlos
%A Menasalvas, Ernestina
%A Spiliopoulou, Myra
%B Discovery Science
%D 2009
%E Gama, João
%E Costa, VítorSantos
%E Jorge, AlípioMário
%E Brazdil, PavelB.
%I Springer Berlin Heidelberg
%K kmd
%P 287-301
%R 10.1007/978-3-642-04747-3_23
%T C-DenStream: Using Domain Knowledge on a Data Stream
%U http://dx.doi.org/10.1007/978-3-642-04747-3_23
%V 5808
%X Stream clustering algorithms are traditionally designed to process streams efficiently and to adapt to the evolution of the underlying population. This is done without assuming any prior knowledge about the data. However, in many cases, a certain amount of domain or background knowledge is available, and instead of simply using it for the external validation of the clustering results, this knowledge can be used to guide the clustering process. In non-stream data, domain knowledge is exploited in the context of
%@ 978-3-642-04746-6
@incollection{noKey,
abstract = {Stream clustering algorithms are traditionally designed to process streams efficiently and to adapt to the evolution of the underlying population. This is done without assuming any prior knowledge about the data. However, in many cases, a certain amount of domain or background knowledge is available, and instead of simply using it for the external validation of the clustering results, this knowledge can be used to guide the clustering process. In non-stream data, domain knowledge is exploited in the context of },
added-at = {2014-06-20T13:44:46.000+0200},
author = {Ruiz, Carlos and Menasalvas, Ernestina and Spiliopoulou, Myra},
biburl = {https://www.bibsonomy.org/bibtex/200d21c06c49b0034292ad88b31becd1c/kmd-ovgu},
booktitle = {Discovery Science},
doi = {10.1007/978-3-642-04747-3_23},
editor = {Gama, João and Costa, VítorSantos and Jorge, AlípioMário and Brazdil, PavelB.},
interhash = {9aad4ae8dca85e4aa220fa875802e2ad},
intrahash = {00d21c06c49b0034292ad88b31becd1c},
isbn = {978-3-642-04746-6},
keywords = {kmd},
language = {English},
pages = {287-301},
publisher = {Springer Berlin Heidelberg},
series = {Lecture Notes in Computer Science},
timestamp = {2014-06-20T13:44:46.000+0200},
title = {C-DenStream: Using Domain Knowledge on a Data Stream},
url = {http://dx.doi.org/10.1007/978-3-642-04747-3_23},
volume = 5808,
year = 2009
}