Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Comparative Study of Clustering Approaches Applied to Spatial or Temporal Pattern Discovery

Version 1 : Received: 7 August 2020 / Approved: 8 August 2020 / Online: 8 August 2020 (18:00:26 CEST)

A peer-reviewed article of this Preprint also exists.

Grassi, K.; Poisson-Caillault, É.; Bigand, A.; Lefebvre, A. Comparative Study of Clustering Approaches Applied to Spatial or Temporal Pattern Discovery. J. Mar. Sci. Eng. 2020, 8, 713. Grassi, K.; Poisson-Caillault, É.; Bigand, A.; Lefebvre, A. Comparative Study of Clustering Approaches Applied to Spatial or Temporal Pattern Discovery. J. Mar. Sci. Eng. 2020, 8, 713.

Journal reference: J. Mar. Sci. Eng. 2020, 8, 713
DOI: 10.3390/jmse8090713

Abstract

Many clustering approaches succeed in pattern segmentation in many applications. This unsupervised segmentation should be effective to reduce an expert labelling time: i.e, they must be able to detect the number of patterns and identify them in a sequence or map with the right cuts. Several direct and hierarchical clustering approaches are compared for this task. A divisive spectral clustering architecture with a no-cut criteria is also proposed. This new algorithm achieves promise segmentation of spatial UCI databases and marine time series compared to other approaches.

Subject Areas

Clustering; Pattern Discovery; Time series; Multi-Level Spectral Clustering; English Channel

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