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Online Clustering of High-Dimensional Trajectories under Concept Drift

G. Krempl, Z. Siddiqui, and M. Spiliopoulou. Machine Learning and Knowledge Discovery in Databases , volume 6912 of Lecture Notes in Computer Science, Springer Berlin Heidelberg, (2011)

Abstract

Historical transaction data are collected in many applications, e.g., patient histories recorded by physicians and customer transactions collected by companies. An important question is the learning of models upon

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DOI:: 10.1007/978-3-642-23783-6_17
URL:: http://dx.doi.org/10.1007/978-3-642-23783-6_17
BibTeX key:: noKey
search on:: Google Scholar Microsoft Bing WorldCat BASE

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Cite this publication

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  abstract = {Historical transaction data are collected in many applications, e.g., patient histories recorded by physicians and customer transactions collected by companies. An important question is the learning of models upon },
  added-at = {2014-06-20T12:32:18.000+0200},
  author = {Krempl, Georg and Siddiqui, Zaigham Faraz and Spiliopoulou, Myra},
  biburl = {https://www.bibsonomy.org/bibtex/20db4969da058a279afae280c9b2b07d2/kmd-ovgu},
  booktitle = {Machine Learning and Knowledge Discovery in Databases},
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  editor = {Gunopulos, Dimitrios and Hofmann, Thomas and Malerba, Donato and Vazirgiannis, Michalis},
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  keywords = {IMPRINT drift-mining kmd},
  language = {English},
  pages = {261-276},
  publisher = {Springer Berlin Heidelberg},
  series = {Lecture Notes in Computer Science},
  timestamp = {2014-09-04T16:02:37.000+0200},
  title = {Online Clustering of High-Dimensional Trajectories under Concept Drift},
  url = {http://dx.doi.org/10.1007/978-3-642-23783-6_17},
  volume = 6912,
  year = 2011
}

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%A Krempl, Georg
%A Siddiqui, Zaigham Faraz
%A Spiliopoulou, Myra
%B Machine Learning and Knowledge Discovery in Databases
%D 2011
%E Gunopulos, Dimitrios
%E Hofmann, Thomas
%E Malerba, Donato
%E Vazirgiannis, Michalis
%I Springer Berlin Heidelberg
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%T Online Clustering of High-Dimensional Trajectories under Concept Drift
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%V 6912
%X Historical transaction data are collected in many applications, e.g., patient histories recorded by physicians and customer transactions collected by companies. An important question is the learning of models upon
%@ 978-3-642-23782-9

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