Abstract
A new feature selection method is proposed for high-dimensional data clustering on the basis of data field. With the potential entropy to evaluate the importance of feature subsets, features are filtered by removing unimportant features or noises from the original datasets. Experiments show that the proposed method can sharply reduce the number of dimensions and effectively improve the clustering performance on WDBC dataset.
Original language | English |
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Pages (from-to) | 661-665 |
Number of pages | 5 |
Journal | Chinese Journal of Electronics |
Volume | 23 |
Issue number | 4 |
Publication status | Published - 1 Oct 2014 |
Keywords
- Data field
- Dimension reduction
- Feature selection
- High-dimensional objects
- Potential entropy