Sparse cluster analysis of large-scale discrete variables with application to single nucleotide polymorphism data
Current extremely large scale genetic data presents significant challenges for cluster analysis. Most existing clustering methods are typically built on Euclidean distance and geared toward analyzing continuous response. They work well for clustering, e.g., microarray gene expression data, but often...
| Veröffentlicht in: | Journal of applied statistics. - 1991. - 40(2013), 2 vom: 01. Feb., Seite 358-367 |
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| Format: | Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2013
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| Zugriff auf das übergeordnete Werk: | Journal of applied statistics |
| Schlagworte: | Journal Article Clustering Expectation-Maximization algorithm K-means Lasso Latent class model Principal components Single nucleotide polymorphism Sparse clustering |