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Automatic Clustering in Large Sets of Time Series

  • Robert AzencottEmail author
  • Viktoria Muravina
  • Rasoul Hekmati
  • Wei Zhang
  • Michael Paldino
Chapter
Part of the Computational Methods in Applied Sciences book series (COMPUTMETHODS, volume 47)

Abstract

To study large sets of interacting time series, we combine spectral analysis of graph Laplacians with simulated annealing to automatically generate optimized clustering of time series, by minimization of cost functions characterizing clustering quality. We apply these techniques to evaluation of connectivity between cortex regions, via analysis of cortex activity recordings by sequences of 3-dimensional fMRI images.

Keywords

Time series clustering Graph Laplacians Spectral clustering Mutual information Simulated annealing kernel k-means 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Robert Azencott
    • 1
    Email author
  • Viktoria Muravina
    • 1
  • Rasoul Hekmati
    • 1
  • Wei Zhang
    • 2
  • Michael Paldino
    • 2
  1. 1.Department of MathematicsUniversity of HoustonHoustonUSA
  2. 2.Neuroradiology SectionTexas Children’s HospitalHoustonUSA

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