Density-based Clustering of Time Series Subsequences

  title={Density-based Clustering of Time Series Subsequences},
  author={Anne M. Denton},
Doubts have been raised that time series subsequences can be clustered in a meaningful way. This paper introduces a kernel-density-based algorithm that detects meaningful patterns in the presence of a vast number of random-walk-like subsequences. The value of density-based algorithms for noise elimination in general has long been demonstrated. The challenge of applying such techniques to time-series data consists in first specifying uninteresting sequences that are to be considered as noise… CONTINUE READING
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Non-linear and non-stationary time series analysis

  • M. Priestley
  • 1988
Highly Influential
3 Excerpts

Kernel density estimation toolbox for matlab

  • A. Ihler
  • 2003
2 Excerpts

The ucr time series data mining archive

  • E. Keogh, T. Folias
  • 2002
3 Excerpts

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