Machine Learning

, Volume 58, Issue 2–3, pp 107–125 | Cite as

Evolutionary Rule Mining in Time Series Databases

  • Magnus Lie Hetland
  • Pål SætromEmail author


Data mining in the form of rule discovery is a growing field of investigation. A recent addition to this field is the use of evolutionary algorithms in the mining process. While this has been used extensively in the traditional mining of relational databases, it has hardly, if at all, been used in mining sequences and time series. In this paper we describe our method for evolutionary sequence mining, using a specialized piece of hardware for rule evaluation, and show how the method can be applied to several different mining tasks, such as supervised sequence prediction, unsupervised mining of interesting rules, discovering connections between separate time series, and investigating tradeoffs between contradictory objectives by using multiobjective evolution.


sequence mining knowledge discovery time series genetic programming specialized hardware 


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Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  1. 1.University of Science and TechnologyNorway
  2. 2.Interagon ASLos Angeles

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