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Inhaltsangabe
Traditionally, statistical work is schematically divided into two stages: one exploratory stage, being based on a range of descriptive and graphic techniques, and one decisional stage, being based on probabilistic models. The exploratory phase, also called data analysis, which is the object of this book is the act of transforming data with the aim of extracting useful information. It is composed of elaborate exploratory methods applying to the multidimensional data and often overflowing the exploratory framework itself. The first part of this book is devoted to methods seeking relevant dimensions of the data. The variables thus obtained provide a synthetic description which often results in a graphical representation of the data. After a general presentation of the discriminating analysis, the second part is devoted to clustering methods which constitute another method, which is often complementary to the methods described in the first part, to synthesize and to analyze the data. The book concludes by examining the links existing between data mining and data analysis.
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Über die Autorin bzw. den Autor
Gerard Govaert is Professor at the University of Technology of Compiegne, France. He is also a member of the CNRS Laboratory Heudiasyc (Heuristic and diagnostic of complex systems). His research interests include latent structure modelling, model selection, model-based cluster analysis, block clustering and statistical pattern recognition. He is one of the authors of the MIXMOD (MIXture MODelling) software.
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