  Clustering of mixed-type datasets can be a particularly challenging task as
it requires taking into account the associations between variables with
different level of measurement, i.e., nominal, ordinal and/or interval. In some
cases, hierarchical clustering is considered a suitable approach, as it makes
few assumptions about the data and its solution can be easily visualized. Since
most hierarchical clustering approaches assume variables are measured on the
same scale, a simple strategy for clustering mixed-type data is to homogenize
the variables before clustering. This would mean either recoding the continuous
variables as categorical ones or vice versa. However, typical discretization of
continuous variables implies loss of information. In this work, an
agglomerative hierarchical clustering approach for mixed-type data is proposed,
which relies on a barycentric coding of continuous variables. The proposed
approach minimizes information loss and is compatible with the framework of
correspondence analysis. The utility of the method is demonstrated on real and
simulated data.
