This article proposes two enhancements of the hierarchical agglomerative clustering with incremental similarity search presented at SISAP 2024. First, we extend the approach to support HDBSCAN*, a density-based clustering algorithm that is related to single-linkage clustering, but offers increased robustness to noise due to its minimum density requirements. Second, we replace the previous Kruskal-based approach with a variant of Borůvka’s minimum spanning tree algorithm, which avoid certain cases where the previous approach would deliver poor runtime performance. Similar to the previous approach, this leverages incremental nearest-neighbor search to accelerate the clustering process if the data is amenable to indexing.