An ensemble method for estimating the number of clusters in a big data set using multiple random sam...
An ensemble method for estimating the number of clusters in a big data set using multiple random samples
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Cham: Springer International Publishing
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English
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Cham: Springer International Publishing
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Clustering a big dataset without knowing the number of clusters presents a big challenge to many existing clustering algorithms. In this paper, we propose a Random Sample Partition-based Centers Ensemble (RSPCE) algorithm to identify the number of clusters in a big dataset. In this algorithm, a set of disjoint random samples is selected from the bi...
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Full title
An ensemble method for estimating the number of clusters in a big data set using multiple random samples
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TN_cdi_doaj_primary_oai_doaj_org_article_fd86b4da6db542a487c3aef204d0855b
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_fd86b4da6db542a487c3aef204d0855b
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ISSN
2196-1115
E-ISSN
2196-1115
DOI
10.1186/s40537-023-00709-4
