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Temporal Data Mining for Smart Homes

  • Mykola Galushka
  • Dave Patterson
  • Niall Rooney
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4008)

Abstract

Temporal data mining is a relatively new area of research in computer science. It can provide a large variety of different methods and techniques for handling and analyzing temporal data generated by smart-home environments. Temporal data mining in general fits into a two level architecture, where initially a transformation technique reduces data dimensionality in the first level and indexing techniques provide efficient access to the data in the second level. This infrastructure of temporal data mining provides the basis for high-level data mining operations such as clustering, classification, rule discovery and prediction. These operations can form the basis for developing different smart-home applications, capable of addressing a number of situations occurring within this environment. This paper outlines the main temporal data mining techniques available and provides examples of where they can be applied within a smart home environment.

Keywords

Association Rule Smart Home Similarity Metrics Rule Discovery Time Series Database 
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 2006

Authors and Affiliations

  • Mykola Galushka
    • 1
  • Dave Patterson
    • 1
  • Niall Rooney
    • 1
  1. 1.Northern Ireland Knowledge Engineering Laboratory (NIKEL)Newtownabbey, Co. AntrimNorthern Ireland

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