In distributed clustering problems, nodes in a wireless sensor network must learn clusters from the data sensed across the network, without centralising the raw data. This paper presents an asynchronous distributed clustering algorithm for sensors to learn the global clusters, while respecting data privacy, and balancing communication cost and clustering quality. Different clustering algorithms including k-means and Gaussian Mixture Models, and different methods of summarising clusters to exchange between nodes are considered. In experiments on randomly generated network topologies, we demonstrate that methods which do more extensive clustering in each cycle, and which exchange descriptions of cluster shape and density instead of just centroids and data counts, achieve more consistent clustering, in significantly shorter elapsed time.