  Clustering has received much attention in Statistics and Machine learning
with the aim of developing statistical models and autonomous algorithms which
are capable of acquiring information from raw data in order to perform
exploratory analysis.Several techniques have been developed to cluster sampled
univariate vectors only considering the average value over the whole period and
as such they have not been able to explore fully the underlying distribution as
well as other features of the data, especially in presence of structured time
series. We propose a model-based clustering technique that is based on quantile
regression permitting us to cluster bivariate time series at different quantile
levels. We model the within cluster density using asymmetric Laplace
distribution allowing us to take into account asymmetry in the distribution of
the data. We evaluate the performance of the proposed technique through a
simulation study. The method is then applied to cluster time series observed
from Glob-colour satellite data related to trophic status indices with aim of
evaluating their temporal dynamics in order to identify homogeneous areas, in
terms of trophic status, in the Gulf of Gabes.
