Chapter

Discovery Science

Volume 4265 of the series Lecture Notes in Computer Science pp 89-101

Mining Approximate Motifs in Time Series

  • Pedro G. FerreiraAffiliated withCarnegie Mellon UniversityDepartment of Informatics, University of Minho
  • , Paulo J. AzevedoAffiliated withCarnegie Mellon UniversityDepartment of Informatics, University of Minho
  • , Cândida G. SilvaAffiliated withCarnegie Mellon UniversityChemistry Department, Faculty of Sciences and Technology, and Centre of Neurosciences of Coimbra, University of Coimbra
  • , Rui M. M. BritoAffiliated withCarnegie Mellon UniversityChemistry Department, Faculty of Sciences and Technology, and Centre of Neurosciences of Coimbra, University of Coimbra

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Abstract

The problem of discovering previously unknown frequent patterns in time series, also called motifs, has been recently introduced. A motif is a subseries pattern that appears a significant number of times. Results demonstrate that motifs may provide valuable insights about the data and have a wide range of applications in data mining tasks. The main motivation for this study was the need to mine time series data from protein folding/unfolding simulations. We propose an algorithm that extracts approximate motifs, i.e. motifs that capture portions of time series with a similar and eventually symmetric behavior. Preliminary results on the analysis of protein unfolding data support this proposal as a valuable tool. Additional experiments demonstrate that the application of utility of our algorithm is not limited to this particular problem. Rather it can be an interesting tool to be applied in many real world problems.