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Classification and Multivariate Analysis for Complex Data Structures
Details
The growing capabilities in generating and collecting data has risen an urgent need of new techniques and tools in order to analyze, classify and summarize statistical information, as well as to discover and characterize trends, and to automatically bag anomalies. This volume provides the latest advances in data analysis methods for multidimensional data which can present a complex structure: The book offers a selection of papers presented at the first Joint Meeting of the Société Francophone de Classification and the Classification and Data Analysis Group of the Italian Statistical Society. Special attention is paid to new methodological contributions from both the theoretical and the applicative point of views, in the fields of Clustering, Classification, Time Series Analysis, Multidimensional Data Analysis, Knowledge Discovery from Large Datasets, Spatial Statistics.
Latest advances in data analysis methods for multidimensional data With contributions by international experts Special attention to new methodological contributions from theoretical and applicative points of view Includes supplementary material: sn.pub/extras
Inhalt
Key Notes.- Classification and Discrimination.- Data Mining.- Robustness and Classification.- Categorical Data and Latent Class Approach.- Latent Variables and Related Methods.- Symbolic, Multivalued and Conceptual Data Analysis.- Spatial, Temporal, Streaming and Functional Data Analysis.- Bio and Health Science.
Weitere Informationen
- Allgemeine Informationen
- GTIN 09783642133114
- Editor Bernard Fichet, Maurizio Vichi, Rosanna Verde, Domenico Piccolo
- Sprache Englisch
- Auflage 2011
- Größe H235mm x B155mm x T27mm
- Jahr 2011
- EAN 9783642133114
- Format Kartonierter Einband
- ISBN 3642133118
- Veröffentlichung 05.03.2011
- Titel Classification and Multivariate Analysis for Complex Data Structures
- Untertitel Studies in Classification, Data Analysis, and Knowledge Organization
- Gewicht 744g
- Herausgeber Springer Berlin Heidelberg
- Anzahl Seiten 496
- Lesemotiv Verstehen
- Genre Mathematik