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• W2227194292 startingPage "e0141756" @default.
• W2227194292 abstract "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." @default.
• W2227194292 created "2016-06-24" @default.
• W2227194292 creator A5018895937 @default.
• W2227194292 creator A5050849172 @default.
• W2227194292 date "2015-10-30" @default.
• W2227194292 modified "2023-09-27" @default.
• W2227194292 title "Clustering Acoustic Segments Using Multi-Stage Agglomerative Hierarchical Clustering" @default.
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• W2227194292 doi "https://doi.org/10.1371/journal.pone.0141756" @default.
• W2227194292 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4627777" @default.
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