© 2015 Lerato, Niesler. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Agglomerative hierarchical clustering becomes infeasible when applied to large datasets due to its O(N2 ) storage requirements. We present a multi-stage agglomerative hierarchical clustering (MAHC) approach aimed at large datasets of speech segments. The algorithm is based on an iterative divide-and-conquer strategy. The data is first split into independent subsets, each of which is clustered separately. Thus reduces the storage required for sequential implementations, and allows concurrent computation on parallel computing hardware. The resultant clusters are merged and subsequently re-divided into subsets, which are passed to the following iteration. We show that MAHC can match and even surpass the performance of the exact implementation when applied to datasets of speech segments.
Lerato, L., & Niesler, T. (2015). Clustering acoustic segments using multi-stage agglomerative hierarchical clustering. PLoS ONE, 10(10). https://doi.org/10.1371/journal.pone.0141756