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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Araujo, D. Neto, A.D. Martins, A. Melo, J. |
| Copyright Year | 2011 |
| Description | Author affiliation: Department of Computing and Automation, Federal University of Rio Grande do Norte, Natal, Brazil (Araujo, D.; Neto, A.D.; Martins, A.; Melo, J.) |
| Abstract | This paper proposes a study on the impact of the use of dimension reduction techniques (DRTs) in the quality of partitions produced by cluster analysis of microarray datasets. We tested seven DRTs applied to four microarray cancer datasets and ran four clustering algorithms using the original and reduced datasets. Overall results showed that using DRTs provides a improvement in performance of all algorithms tested, specially in the hierarchical class. We could see that, despite Principal Component Analysis (PCA) being the most widely used DRT, its was overcome by other nonlinear methods and it did not provide a substantial performance increase in the clustering algorithms. On the other hand, t-distributed Stochastic Embedding (t-SNE) and Laplacian Eigenmaps (LE) achieved good results for all datasets. |
| Starting Page | 1835 |
| Ending Page | 1842 |
| File Size | 440864 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424496358 |
| ISSN | 21614407 |
| e-ISBN | 9781424496372 |
| DOI | 10.1109/IJCNN.2011.6033447 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-07-31 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Clustering algorithms Principal component analysis Partitioning algorithms Algorithm design and analysis Indexes Kernel Cancer |
| Content Type | Text |
| Resource Type | Article |
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