  Clustering can be used to extract insights from data or to verify some of the
assumptions held by the domain experts, namely data segmentation. In the
literature, few methods can be applied in clustering qualitative values using
the context associated with other variables present in the data, without losing
interpretability. Moreover, the metrics for calculating dissimilarity between
qualitative values often scale poorly for high dimensional mixed datasets.
  In this study, we propose a novel method for clustering qualitative values,
based on Hierarchical Clustering (HQC), and using Maximum Mean Discrepancy. HQC
maintains the original interpretability of the qualitative information present
in the dataset. We apply HQC to two datasets. Using a mixed dataset provided by
Spotify, we showcase how our method can be used for clustering music artists
based on the quantitative features of thousands of songs. In addition, using
financial features of companies, we cluster company industries, and discuss the
implications in investment portfolios diversification.
