Classification and Multivariate Analysis for Complex Data Structures
Bernard Fichet, Domenico Piccolo, Rosanna Verde, Maurizio Vichi
Springer Science & Business Media, Mar 4, 2011 - Mathematics - 473 pages
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.
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Part II Classification and Discrimination
Part III Data Mining
Part IV Robustness and Classification
Part V Categorical Data and Latent Class Approach
Part VI Latent Variables and Related Methods
Part VII Symbolic Multivalued and Conceptual Data Analysis
Part VIII Spatial Temporal Streaming and Functional Data Analysis
Part IX Bio and Health Science
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AdaBoost algorithm Analysis for Complex application approach archetypes B-spline Berlin Heidelberg 2011 bicluster binary categorical Classification and Multivariate clustering coefficients Complex Data Structures computed Conjoint Analysis considered correlation corresponding covariance criterion Data Analysis data set defined denoted distance distribution ECTD error estimation evaluate factor Fichet forward search function gene global groups histogram interval Italy itemset iterative Knowledge Organization latent class latent class model latent variable linear matrix mean measure metabins method multidimensional multidimensional scaling Multivariate Analysis node objects observations obtained optimal parameters partition performed points Poisson process prediction predictors Principal Component Analysis problem procedure proposed random References regression response variable sample scaling scores Sect simulation split Springer Springer-Verlag Berlin Heidelberg Stat statistical step Studies in Classification subset symbolic data Table technique threshold tion tree users values variance vector visualization weights