Robust methods for data reduction / Alessio Farcomeni, Luca Greco
Series: Data MiningPublication details: Boca Raton, Fla. : CRC Press, 2020Description: xxvii, 269 p. : ill., charts ; 24 cmISBN:- 9780367827779 :
- 23rd R.R. 629.8 F 222
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National Library of India Reading Room - Main Reading Room | Reading Room | R.R. 629.8 F 222 (Browse shelf(Opens below)) | HB | Available | PUR000579560ENG |
Robust Methods for Data Reduction gives a non-technical overview of robust data reduction techniques, encouraging the use of these important and useful methods in practical applications. The main areas covered include principal components analysis, sparse principal component analysis, canonical correlation analysis, factor analysis, clustering, double clustering, and discriminant analysis. The first part of the book illustrates how dimension reduction techniques synthesize available information by reducing the dimensionality of the data. The second part focuses on cluster and discriminant analysis
Includes bibliographical references and indexes
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