  We present a novel method that can learn a graph representation from
multivariate data. In our representation, each node represents a cluster of
data points and each edge represents the subset-superset relationship between
clusters, which can be mutually overlapped. The key to our method is to use
formal concept analysis (FCA), which can extract hierarchical relationships
between clusters based on the algebraic closedness property. We empirically
show that our method can effectively extract hierarchical structures of
clusters compared to the baseline method.
