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Reducing the Time Requirement of k-Means Algorithm

Traditional k-means and most k-means variants are still computationally expensive for large datasets, such as microarray data, which have large datasets with large dimension size d. In k-means clustering, we are given a set of n data points in d-dimensional space R(d) and an integer k. The problem i...

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Détails bibliographiques
Auteurs principaux: Osamor, Victor Chukwudi, Adebiyi, Ezekiel Femi, Oyelade, Jelilli Olarenwaju, Doumbia, Seydou
Format: Artigo
Langue:Inglês
Publié: Public Library of Science 2012
Sujets:
Accès en ligne:https://ncbi.nlm.nih.gov/pmc/articles/PMC3519838/
https://ncbi.nlm.nih.gov/pubmed/23239974
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0049946
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