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Clustering acoustic emission data streams with sequentially appearing clusters using mixture models. (arXiv:2108.11211v3 [stat.ML] UPDATED)
June 16, 2022, 1:12 a.m. | Emmanuel Ramasso, Thierry Denoeux, Gael Chevallier
stat.ML updates on arXiv.org arxiv.org
The interpretation of unlabeled acoustic emission (AE) data classically
relies on general-purpose clustering methods. While several external criteria
have been used in the past to select the hyperparameters of those algorithms,
few studies have paid attention to the development of dedicated objective
functions in clustering methods able to cope with the specificities of AE data.
We investigate how to explicitly represent clusters onsets in mixture models in
general, and in Gaussian Mixture Models (GMM) in particular. By modifying the
internal …
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