FN Archimer Export Format
PT J
TI Comparative Study of Clustering Approaches Applied to Spatial or Temporal Pattern Discovery
BT 
AF GRASSI, Kelly
   POISSON-CAILLAULT, Emilie
   BIGAND, André
   LEFEBVRE, Alain
AS 1:1,2,3;2:2;3:2;4:3;
FF 1:PDG-ODE-LITTORAL-LERBL;2:;3:;4:PDG-ODE-LITTORAL-LERBL;
C1 Weather Force, Toulouse, France
   LISIC EA 4491 Univ. Littoral, Calais, France
   IFREMER ,LER-BL Boulogne-sur-mer, France
C2 WEATHERFORCE, FRANCE
   UNIV LITTORAL COTE D'OPALE, FRANCE
   IFREMER, FRANCE
SI BOULOGNE
SE PDG-ODE-LITTORAL-LERBL
IN WOS Ifremer UPR
   DOAJ
   copubli-france
   copubli-univ-france
IF 2.744
TC 1
UR https://archimer.ifremer.fr/doc/00643/75481/76326.pdf
   https://archimer.ifremer.fr/doc/00643/75481/77063.pdf
LA English
DT Article
DE ;clustering;pattern discovery;time series;Multi-Level Spectral Clustering;English Channel
AB In the framework of ecological or environmental assessments and management, detection, characterization and forecasting of the dynamics of environmental states are of paramount importance. These states should reflect general patterns of change, recurrent or occasional events, long-lasting or short or extreme events which contribute to explain the structure and the function of the ecosystem. To identify such states, many scientific consortiums promote the implementation of Integrated Observing Systems which generate increasing amount of complex multivariate/multisource/multiscale datasets. Extracting the most relevant ecological information from such complex datasets requires the implementation of Machine Learning-based processing tools. In this context, we proposed a divisive spectral clustering architecture—the Multi-level Spectral Clustering (M-SC) which is, in this paper, extended with a no-cut criteria. This method is developed to perform detection events for data with a complex shape and high local connexity. While the M-SC method was firstly developed and implemented for a given specific case study, we proposed here to compare our new M-SC method with several existing direct and hierarchical clustering approaches. The clustering performance is assessed from different datasets with hard shapes to segment. Spectral methods are most efficient discovering all spatial patterns. For the segmentation of time series, hierarchical methods better isolated event patterns. The new M-SC algorithm, which combines hierarchical and spectral approaches, give promise results in the segmentation of both spatial UCI databases and marine time series compared to other approaches. The ability of our M-SC method to deal with many kinds of datasets allows a large comparability of results if applies within a broad Integrated Observing Systems. Beyond scientific knowledge improvements, this comparability is crucial for decision-making about environmental management. 
PY 2020
PD SEP
SO Journal Of Marine Science And Engineering
SN 2077-1312
PU MDPI AG
VL 8
IS 9
UT 000581691600001
DI 10.3390/jmse8090713
ID 75481
ER

EF