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A framework for temporal data mining

  • Xiaodong Chen
  • Ilias Petrounias
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1460)

Abstract

Time is an important aspect of all real world phenomena. Any systems, approaches or techniques that are concerned with information need to take into account the temporal aspect of data. Data mining refers to a set of techniques for discovering previously unknown information from existing data in large databases and therefore, the information discovered will be of limited value if its temporal aspects, i.e. validity, periodicity, are not considered. This paper presents a generic definition of temporal patterns and a framework for discovering them. An architecture for the mining of such patterns is presented along with a temporal query language for extracting them from a database. As an instance of generic patterns, temporal association rules are used as examples of the proposed approach.

Keywords

Association Rule Time Expression Mining Task Temporal Database Data Mining Task 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Xiaodong Chen
    • 1
  • Ilias Petrounias
    • 2
  1. 1.Department of Computing & MathematicsManchester Metropolitan UniversityManchesterUK
  2. 2.Department of ComputationUMISTManchesterUK

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