  In multiple correspondence analysis, both individuals (observations) and
categories can be represented in a biplot that jointly depicts the
relationships across categories or individuals, as well as the associations
between them. Additional information about the individuals can enhance
interpretation capacities, such as by including categorical variables for which
the interdependencies are not of immediate concern, but that facilitate the
interpretation of the plot with respect to relationships between individuals
and categories. This article proposes a new method for adding such information,
according to a multiple-set cluster correspondence analysis approach that
identifies clusters specific to classes, or subsets of the data that correspond
to the categories of the additional variables. The proposed method can
construct a biplot that depicts heterogeneous tendencies of individual members,
as well as their relationships with the original categorical variables. A
simulation study to investigate the performance of this proposed method and an
application to data regarding road accidents in the United Kingdom confirms the
viability of this approach.
