Please use this identifier to cite or link to this item:
http://hdl.handle.net/10773/16461| Title: | Statistical Methods and Optimization in Data Mining |
| Author: | Macedo, Eloísa Freitas, Adelaide |
| Keywords: | Principal component analysis Clustering Data mining |
| Issue Date: | Sep-2012 |
| Publisher: | University of Évora, Dorodnicyn Computing Centre of Russian Academy of Sciences |
| Abstract: | The main objective of this work is to test the ability of the new tech- nique CDPCA - Clustering and Disjoint Principal Component Analysis on biological data sets to make possible visual representation of relevant characteristics for data interpretation. For this purpose, we im- plemented CDPCA in R language and conducted several experiments. Numerical results show its efficiency. |
| Peer review: | yes |
| URI: | http://hdl.handle.net/10773/16461 |
| ISBN: | 978–5–91601–051–0 |
| Publisher Version: | http://www.cima.uevora.pt/optima2012/Art/Macedo.pdf |
| Appears in Collections: | CIDMA - Comunicações |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| macedo,freitas_OPTIMA2012proceedings_posprint.pdf | 87.55 kB | Adobe PDF | View/Open |
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