Classification and multivariate analysis for complex data structures
Author(s)
Bibliographic Information
Classification and multivariate analysis for complex data structures
(Studies in classification, data analysis, and knowledge organization)
Springer, c2011
Available at 4 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
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  United States of America
Note
Includes bibliographical references
"a selection of revised 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 (SFC-CLADAG 2008) which was held in Caserta, June 11-13, 2008."--Preface
Description and Table of Contents
Description
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 Societe 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.
Table of Contents
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.
by "Nielsen BookData"