Titlebook: Classification and Multivariate Analysis for Complex Data Structures; Bernard Fichet,Domenico Piccolo,Maurizio Vichi Conference proceeding

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Conference proceedings 2011ecial attention is paid to new methodological contributions fromboth 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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1431-8814 he applicative point of views, in the fields of Clustering, Classification, Time Series Analysis, Multidimensional Data Analysis, Knowledge Discovery from Large Datasets, Spatial Statistics.978-3-642-13311-4978-3-642-13312-1Series ISSN 1431-8814 Series E-ISSN 2198-3321

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https://doi.org/10.1007/978-3-031-09581-8data table loose the histograms constraints and suppose independencies between the bins but copulas takes care of the probabilities and the underlying dependencies. Then, we give several ways for representing the units (called “individuals”), the bins, the variables and the metabins when the number

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Menno L. W. Knetsch,Thomas J. Cleije-dimensional continuum. An indication is given for how the overall process converges to a (possibly local) optimum. As an illustration, the method is applied to the analysis of a published observational data set.

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Herco Fonteijn,Arie van der Lugtclusters. We present two likelihood ratio tests for the number of clusters based on the Poisson processes. Most of these clustering methods and tests for the number of clusters have been extended to symbolic data.

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Menno L. W. Knetsch,Thomas J. Cleijon with a fast algorithm is applied: the best split of the objects is found in the set of candidate (prospective) splits of each predictor modalities by maximizing the predictability of the two-class response. Some applications on real world cases and a simulation study allow to demonstrate that the

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