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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...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Public Library of Science
2012
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3519838/ https://www.ncbi.nlm.nih.gov/pubmed/23239974 http://dx.doi.org/10.1371/journal.pone.0049946 |