Graph learning (GL) aims to infer the topology of an unknown graph from a set of observations on its nodes, i.e., graph signals. While most of the existing GL approaches focus on homogeneous datasets, in many real world applications, data is heterogeneous, where graph signals are clustered and each cluster is associated with a different graph. In this paper, we address the problem of learning multiple graphs from heterogeneous data by formulating an optimization problem for joint graph signal clustering and graph topology inference. In particular, our approach extends spectral clustering by partitioning the graph signals not only based on their pairwise similarities but also their smoothness with respect to the graphs associated with the clusters. The proposed method also learns the representative graph for each cluster using the smoothness of the graph signals with respect to the graph topology. The resulting optimization problem is solved with an efficient block-coordinate descent algorithm and results on simulated and real data indicate the effectiveness of the proposed method.