Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data

By H. Bock, E. Diday. 2000

646 citations recorded by the source corpus.

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Citations in the corpus

Centre and Range method for fitting a linear regression model to symbolic interval data (2008, methodology): “…of SDA is to provide suitable methods (clustering, factorial techniques, decision trees, etc.) for managing aggregated data described by multi-valued variables, for which the cells of the data table contain sets of categories, intervals or weight (probability) distributions (Bock and Diday, 2000).”
A least squares approach to principal component analysis for interval valued data (2004, background): “PCA) are multi-step procedures which aim at detecting the underlying structure of two-way interval valued data sets [ 1-2 ].”
From the Statistics of Data to the Statistics of Knowledge (2003, methodology): “An extensive coverage of earlier results has been given by Bock and Diday (2000).”
Decision trees for mining data streams (2006, unknown): “InHoeffding Based on this idea we present two methods to estimate the class-distributions at each node of the decision tree: one based on pessimistic estimates of the error,the other based on the afﬁnity coefﬁcient [2].”
Symbolic Data Analysis: Conceptual Statistics and Data Mining (Wiley Series in Computational Statistics) (2007, background): “In some sense this volume owes its origins to the review paper by Billard and Diday (2003) and the edited volume by Bock and Diday (2000).”
Component Models for Fuzzy Data (2006, methodology): “The most common data reduction methods for interval valued data are the Centers-Principal Component Analysis (hereinafter C-PCA) and the Vertices-Principal Component Analysis (hereinafter V-PCA) proposed by Cazes, Chouakria, Diday, and Schektman (1997) (see also Bock & Diday, 2000; Lauro & Palumbo”
Modelling interval data with Normal and Skew-Normal distributions (2012, background): “Symbolic data analysis 5 7 11 provides a framework where new variable types allow to take directly into account variability and/or uncertainty associated to each single “individual”, avoiding restrictive summarizations to impose a fit to the classical representation structure.”