Monitoring and interpretation of changing patterns is a task of paramount importance for data mining applications in dynamic environments. While there is much research in adapting patterns in the presence of drift or shift, there is less research on how to maintain an overview of pattern changes over time. A major challenge lays in summarizing changes in an effective way, so that the nature of change can be understood by the user, while the demand on resources remains low. To this end, we propose FINGERPRINT, an environment for the summarization of cluster evolution. Cluster changes are captured into an “evolution graph”, which is then summarized based on cluster similarity into a
%0 Book Section
%1 noKey
%A Ntoutsi, Irene
%A Spiliopoulou, Myra
%A Theodoridis, Yannis
%B Computational Science and Its Applications - ICCSA 2011
%D 2011
%E Murgante, Beniamino
%E Gervasi, Osvaldo
%E Iglesias, Andrés
%E Taniar, David
%E Apduhan, BernadyO.
%I Springer Berlin Heidelberg
%K kmd
%P 562-577
%R 10.1007/978-3-642-21887-3_43
%T Summarizing Cluster Evolution in Dynamic Environments
%U http://dx.doi.org/10.1007/978-3-642-21887-3_43
%V 6783
%X Monitoring and interpretation of changing patterns is a task of paramount importance for data mining applications in dynamic environments. While there is much research in adapting patterns in the presence of drift or shift, there is less research on how to maintain an overview of pattern changes over time. A major challenge lays in summarizing changes in an effective way, so that the nature of change can be understood by the user, while the demand on resources remains low. To this end, we propose FINGERPRINT, an environment for the summarization of cluster evolution. Cluster changes are captured into an “evolution graph”, which is then summarized based on cluster similarity into a
%@ 978-3-642-21886-6
@incollection{noKey,
abstract = {Monitoring and interpretation of changing patterns is a task of paramount importance for data mining applications in dynamic environments. While there is much research in adapting patterns in the presence of drift or shift, there is less research on how to maintain an overview of pattern changes over time. A major challenge lays in summarizing changes in an effective way, so that the nature of change can be understood by the user, while the demand on resources remains low. To this end, we propose FINGERPRINT, an environment for the summarization of cluster evolution. Cluster changes are captured into an “evolution graph”, which is then summarized based on cluster similarity into a },
added-at = {2014-06-20T12:34:50.000+0200},
author = {Ntoutsi, Irene and Spiliopoulou, Myra and Theodoridis, Yannis},
biburl = {https://www.bibsonomy.org/bibtex/2b4fbe2a9c05ba7cb60fed5d109597024/kmd-ovgu},
booktitle = {Computational Science and Its Applications - ICCSA 2011},
doi = {10.1007/978-3-642-21887-3_43},
editor = {Murgante, Beniamino and Gervasi, Osvaldo and Iglesias, Andrés and Taniar, David and Apduhan, BernadyO.},
interhash = {6e50ab514e6fe549b305d2d752198a18},
intrahash = {b4fbe2a9c05ba7cb60fed5d109597024},
isbn = {978-3-642-21886-6},
keywords = {kmd},
language = {English},
pages = {562-577},
publisher = {Springer Berlin Heidelberg},
series = {Lecture Notes in Computer Science},
timestamp = {2014-06-20T12:34:50.000+0200},
title = {Summarizing Cluster Evolution in Dynamic Environments},
url = {http://dx.doi.org/10.1007/978-3-642-21887-3_43},
volume = 6783,
year = 2011
}