{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":1,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":20,"n_cols":5,"rows_kept":20,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[2,4,2,5],[3,2,3,3],[5,2,5,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[9,2,9,3],[12,2,12,3],[13,2,13,3],[17,2,17,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9910788117103321"]
[null,null,"Autore",null,"Farcomeni Alessio"]
[null,null,"Titolo",null,"Robust methods for data reduction / / Alessio Farcomeni, Luca Greco"]
[null,null,"Pubbl/distr/stampa",null,"Boca Raton : , : CRC Press, , [2015]"]
[{"$nulls":4},"©2015"]
[null,null,"ISBN",null,"0-429-16796-2"]
[null,null,"Descrizione fisica",null,"1 online resource (297 p.)"]
[null,null,"Collana",null,"Chapman & Hall Book"]
[null,null,"Disciplina",null,"519.5/0285"]
[null,null,"Soggetti",null,"Robust control"]
[{"$nulls":4},"Data reduction - Computer programs"]
[{"$nulls":4},"Dimension reduction (Statistics)"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references."]
[null,null,"Nota di contenuto",null,"Front Cover; Dedication; Contents; Preface; Authors; List of Figures; List of Tables; List of Examples and R illustrations; Symbol Description; 1. Introduction and Overview; 2. Multivariate Estimation Methods; Section I: Dimension Reduction; Introduction to Dimension Reduction; 3. Principal Component Analysis; 4. Sparse Robust PCA; 5. Canonical Correlation Analysis; 6. Factor Analysis; Section II: Sample Reduction; Introduction to Sample Reduction; 7. k-means and Model-Based Clustering; 8. Robust Clustering; 9. Robust Model-Based Clustering; 10. Double Clustering; 11. Discriminant Analysis"]
[{"$nulls":4},"A. Use of the Software R for Data ReductionBibliography"]
[null,null,null,"Sommario/riassunto","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 analy"]