  In most cases, mortality is analysed considering summary indicators (e.~g.
$e_0$ or $e^{\dagger}_0$) that either focus on a specific mortality component
or pool all component-specific information in one measure. This can be a
limitation, when we are interested to analyse the global evolution of mortality
patterns without loosing sight of specific components evolution. The paper
analyses whether there are different patterns of mortality decline among
developed countries, identifying the role played by all the mortality
components. We implement a cluster analysis using a Functional Data Analysis
(FDA) approach, which allows us to consider age-specific mortality rather than
summary measures as it analyses curves rather than scalar data. Combined with a
Functional Principal Component Analysis (PCA) method it can identify what part
of the curves (mortality components) is responsible for assigning one country
to a specific cluster. FDA clustering is applied to 32 countries of Human
Mortality Database and years 1960--2010. The results show that the evolutions
of developed countries follow the same pattern (with different timing): (1) a
reduction of infant mortality, (2) an increase of premature mortality, (3) a
shift and compression of deaths. Some countries are following this scheme and
recovering the gap with precursors, others do not show signs of recovery.
Eastern Europe countries are still at stage (2) and it is not clear if and when
they will enter into phase (3). All the country differences relates the
different timing with which countries undergo the stages identified by
clusters. The cluster analysis based on FDA allows therefore a comprehensive
understanding of the patterns of mortality decline for considered countries.
