Expands methods of knowledge discovery based on visual means
Generates new lossless visual representations of n-D data in 2-D that fully preserves n-D data with focus on Machine Learning/ Data Mining goals, in contrast with a generic visualization without a clearly specified goal
Provides clear interpretation of features of visual representations in terms of n-D data properties
Effectively usrees human vision capabilities of shape perception in mapping n-D data points into 2-D graphs
Recognizes n-D data structures such as hyper-tubes, hyper-planes, hyper-spheres, etc. using lossless visual data representations
Generates new lossless visual representations of n-D data in 2-D that fully preserves n-D data with focus on Machine Learning/ Data Mining goals, in contrast with a generic visualization without a clearly specified goal
Provides clear interpretation of features of visual representations in terms of n-D data properties
Effectively usrees human vision capabilities of shape perception in mapping n-D data points into 2-D graphs
Recognizes n-D data structures such as hyper-tubes, hyper-planes, hyper-spheres, etc. using lossless visual data representations
| Author: Boris Kovalerchuk |
| Publisher: Springer |
| Publication Date: Jan 26, 2018 |
| Number of Pages: 317 pages |
| Binding: Hardback or Cased Book |
| ISBN-10: 3319730398 |
| ISBN-13: 9783319730394 |
Related Products
