Data Analysis

<p>Preface xiii</p>
<p>Chapter 1. Principal Component Analysis: Application to Statistical Process Control 1<br /> Gilbert SAPORTA, Nd&egrave;ye NIANG</p>
<p>1.1. Introduction 1</p>
<p>1.2. Data table and related subspaces 2</p>
<p>1.3. Principal component analysis 8</p>
<p>1.4. Interpretation of PCA results 11</p>
<p>1.5. Application to statistical process control 18</p>
<p>1.6. Conclusion 22</p>
<p>1.7. Bibliography 23</p>
<p>Chapter 2. Correspondence Analysis: Extensions and Applications to the Statistical Analysis of Sensory Data 25<br /> J&eacute;r&ocirc;me PAG&Egrave;S</p>
<p>2.1. Correspondence analysis 25</p>
<p>2.2. Multiple correspondence analysis 39</p>
<p>2.3. An example of application at the crossroads of CA and MCA 50</p>
<p>2.4. Conclusion: two other extensions 63</p>
<p>2.5. Bibliography 64</p>
<p>Chapter 3. Exploratory Projection Pursuit 67<br /> Henri CAUSSINUS, Anne RUIZ–GAZEN</p>
<p>3.1. Introduction 67</p>
<p>3.2. General principles 68</p>
<p>3.3. Some indexes of interest: presentation and use 71</p>
<p>3.4. Generalized principal component analysis 76</p>
<p>3.5. Example 81</p>
<p>3.6. Further topics 86</p>
<p>3.7. Bibliography 89</p>
<p>Chapter 4. The Analysis of Proximity Data 93<br /> Gerard D AUBIGNY</p>
<p>4.1. Introduction 93</p>
<p>4.2. Representation of proximity data in a metric space 97</p>
<p>4.3. Isometric embedding and projection 103</p>
<p>4.4. Multidimensional scaling and approximation 108</p>
<p>4.5. Afielded application 122</p>
<p>4.6. Bibliography 139</p>
<p>Chapter 5. Statistical Modeling of Functional Data 149<br /> Philippe BESSE, Herv&eacute; CARDOT</p>
<p>5.1. Introduction 149</p>
<p>5.2. Functional framework152</p>
<p>5.3. Principal components analysis 156</p>
<p>5.4. Linear regression models and extensions 161</p>
<p>5.5. Forecasting 169</p>
<p>5.6. Concluding remarks 176</p>
<p>5.7. Bibliography 177</p>
<p>Chapter 6. Discriminant Analysis 181<br /> Gilles CELEUX</p>
<p>6.1. Introduction 181</p>
<p>6.2. Main steps in supervised classification 182</p>
<p>6.3. Standard methods in supervised classification 190</p>
<p>6.4. Recent advances 204</p>
<p>6.5. Conclusion 211</p>
<p>6.6. Bibliography 212</p>
<p>Chapter 7. Cluster Analysis 215<br /> Mohamed NADIF, G&eacute;rard GOVAERT</p>
<p>7.1. Introduction 215</p>
<p>7.2. General principles 217</p>
<p>7.3. Hierarchical clustering 224</p>
<p>7.4. Partitional clustering: the k–means algorithm 233</p>
<p>7.5. Miscellaneous clustering methods 239</p>
<p>7.6. Block clustering 245</p>
<p>7.7. Conclusion 251</p>
<p>7.8. Bibliography 251</p>
<p>Chapter 8. Clustering and the Mixture Model 257<br /> G&eacute;rard GOVAERT</p>
<p>8.1. Probabilistic approaches in cluster analysis 257</p>
<p>8.2. The mixture model 261</p>
<p>8.3. EM algorithm 263</p>
<p>8.4. Clustering and the mixture model 267</p>
<p>8.5.Gaussian mixture model 271</p>
<p>8.6. Binary variables 275</p>
<p>8.7. Qualitative variables 279</p>
<p>8.8. Implementation 282</p>
<p>8.9. Conclusion 284</p>
<p>8.10. Bibliography 284</p>
<p>Chapter 9. Spatial Data Clustering 289<br /> Christophe AMBROISE, Mo DANG</p>
<p>9.1. Introduction 289</p>
<p>9.2. Non–probabilistic approaches 293</p>
<p>9.3. Markov random fields as models 295</p>
<p>9.4. Estimating the parameters for a Markov field 305</p>
<p>9.5. Application to numerical ecology 313</p>
<p>9.6. Bibliography 316</p>
<p>List of Authors 319</p>
<p>Index 323</p>