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.
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.
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.
Keywords
Clustering; Pattern Discovery; Time series; Multi-Level Spectral Clustering; English Channel
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
