Publication Date: June 2009 Hardback 352 pp.
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, 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.
1. Principal component analysis: application to statistical process control, Gilbert Saporta and Ndèye Niang.
2. Correspondance analysis: extensions and applications to the statistical analysis of sensory data, Jérôme Pagès.
3. Exploratory projection pursuit, Henri Caussinus and Anne Ruiz-Gazen.
4. The analysis of proximity data, Gérard Drouey d'Aubigny.
5. Statistical modeling of functional data, Philippe Besse and Hervé Cardot.
6. Discriminant analysis, Gilles Celeux.
7. Cluster analysis, Mohamed Nadif and Gérard Govaert.
8. Clustering and the mixture model, Gérard Govaert.
9. Spatial data clustering, Christophe Ambroise and Mo Dang.
11. Data mining and data analysis, Georges Hébrail and Yves Lechevallier.
About the Authors
Gérard Govaert is Professor at the University of Technology of Compiègne, France. He is also a member of the CNRS Laboratory Heudiasyc (Heuristic and diagnostic of complex systems).
His research interests include latent structure modeling, model selection, model-based cluster analysis, block clustering and statistical pattern recognition. He is one of the authors of the MIXMOD (MIXture MODelling) software.