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A Survey of Temporal Knowledge Discovery Paradigms and Methods

J. Roddick, and M. Spiliopoulou. IEEE Trans. of Knowledge and Data Engineering 14 (4): 750-767 (July 2002)

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@article{RoddickSpiliopoulou:tkde02,
  added-at = {2014-06-23T10:49:21.000+0200},
  author = {Roddick, John F. and Spiliopoulou, Myra},
  biburl = {https://www.bibsonomy.org/bibtex/28bbe2a1043cc3e191203d952739e6b79/kmd-ovgu},
  interhash = {cb317342236efb6a1bed8996145ef595},
  intrahash = {8bbe2a1043cc3e191203d952739e6b79},
  journal = {IEEE Trans. of Knowledge and Data Engineering},
  keywords = {kmd},
  month = {Jul.-Aug.},
  number = 4,
  optabstract = {With the increase in the size of datasets, data mining
has recently become an important research topic and is receiving
substantial interest from both academia and industry. At the same
time, interest in temporal databases has been increasing and an
increasing number of both prototype and implemented systems are using
an enhanced temporal understanding to explain aspects of behaviour
associated with the implicit time-varying nature of the universe.
This paper investigates the confluence of these two areas, surveys
the work to date, and explores the issues involved and the
outstanding problems in temporal data mining.},
  pages = {750-767},
  timestamp = {2014-06-23T10:49:21.000+0200},
  title = {A Survey of Temporal Knowledge Discovery Paradigms
and Methods},
  volume = 14,
  year = 2002
}

%0 Journal Article
%1 RoddickSpiliopoulou:tkde02
%A Roddick, John F.
%A Spiliopoulou, Myra
%D 2002
%J IEEE Trans. of Knowledge and Data Engineering
%K kmd
%N 4
%P 750-767
%T A Survey of Temporal Knowledge Discovery Paradigms
and Methods
%V 14

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