chapter 1 The Role of Bayesian and Frequentist Multivariate Modeling in Statistical Data Mining S. James Press University of California, Riverside, USA -- chapter 2 Intelligent Statistical Data Mining with Information Complexity and Genetic Algorithms Hamparsum Bozdogan University of Tennessee, Knoxville, USA -- chapter 3 Econometric and Statistical Data Mining, Prediction and Policy-Making Arnold Zellner University of Chicago, Chicago, USA -- chapter 4 Data Mining Strategies for the Detection of Chemical Warfare Agents / chapter 5 Disclosure Limitation Methods Based on Bounds for Large Contingency Tables With Applications to Disability Durham, University of Washington, Seattle, and Carnegie-Mellon University, / chapter 6 Partial Membership Models with Application to Disability Survey Data Elena A. Erosheva University of Washington, USA -- chapter 7 Automated Scoring of Polygraph Data Aleksandra B. Slavkovic Department of Statistics, Carnegie-Mellon University, / chapter 8 Missing Value Algorithms in Decision Trees Hyunjoong Kim and Sumer Yates University of Tennessee, Knoxville, USA -- chapter 9 Unsupervised Learning from Incomplete Data Using a Mixture Model Approach Lynette Hunt and Murray Jorgensen University of Waikato, Hamilton, / chapter 10 Improving the Performance of Radial Basis Function (RBF) Classification Using Information Criteria Zhenqiu Liu and Hamparsum Bozdogan University of Tennessee, Knoxville, USA -- chapter 11 Use of Kernel Based Techniques for Sensor Validation in Nuclear Robert E. Uhrig University of Tennessee, Knoxville, USA / chapter 12 Data Mining and Traditional Regression of Central Florida, Orlando, FL, USA / chapter 13 An Extended Sliced Inverse Regression Masahiro Mizuta Hokkaido University, Sapporo, Japan -- chapter 14 Using Genetic Programming to Improve the Group Method of Data Handling in Time Series Prediction M. Hiassat, M.F. Abbod, and N. Mort University of Sheffield, Sheffield, UK -- chapter 15 Data Mining for Monitoring Plant Devices Using GMDH and Pattern Classification B.R. Upadhyaya and B. Lu University of Tennessee, Knoxville -- chapter 16 Statistical Modeling and Data Mining to Identify Consumer Preferences Francois Boussu1 and Jean Jacques Denimal2 1Ecole Nationale Superieure des Arts et Industries Textiles, Roubaix, and 2University of Sciences and Technologies of Lille, France -- chapter 17 Testing for Structural Change Over Time of Brand Attribute Perceptions in Market Segments Sara Dolnic?ar and Friedrich Leisch University of Wollongong, and Vienna University of Technology, Austria -- chapter 18 Kernel PCA for Feature Extraction with Information Complexity Zhenqiu Liu and Hamparsum Bozdogan University of Tennessee, Knoxville, USA -- chapter 19 Global Principal Component Analysis for Dimensionality Reduction in Distributed Data Mining Hairong Qi, Tse-Wei Wang, and J. Douglas Birdwell University of Tennessee, Knoxville, USA -- chapter 20 A New Metric for Categorical Data S. H. Al-Harbi, G. P. McKeown and V. J. Rayward-Smith University of / chapter 21 Ordinal Logistic Modeling Using ICOMP as a Goodness-of-Fit Criterion J. Michael Lanning and Hamparsum Bozdogan University of Tennessee, Knoxville, USA -- chapter 22 Comparing Latent Class Factor Analysis with the Traditional Approach in Data Mining Jay Magidson and Jeroen Vermunt Statistical Innovations Incorporated, USA, and Tilburg University, The Netherlands -- chapter 23 On Cluster Effects in Mining Complex Econometric Data / chapter 24 Neural Network-Based Data Mining Techniques for Steel Making Institute of Technology, Cambridge, USA / chapter 25 Solving Data Clustering Problem as a String Search Problem / chapter 26 Behavior-Based Recommender Systems as Value-Added Services for Scientific Libraries / chapter 27 GTP (General Text Parser) Software for Text Mining Justin T. Giles, Ling Wo, and Michael W. Berry University of Tennessee, Knoxville, USA -- chapter 28 Implication Intensity: From the Basic Statistical Definition to the Entropic Version Polytechnique de l�Universite de Nantes, France / chapter 29 Use of a Secondary Splitting Criterion in Classification Forest Construction / chapter 30 A Method Integrating Self-Organizing Maps to Predict the Probability of Barrier Removal Zhicheng Zhang and Fre�de�ric Vanderhaegen University of Valenciennes, / chapter 31 Cluster Analysis of Imputed Financial Data Using an Augmentation-Based Algorithm H. Bensmail and R. P. DeGennaro University of Tennessee, Knoxville, / chapter 32 Data Mining in Federal Agencies Rockville, MD, and University of Pittsburgh, Pittsburgh, PA, USA / chapter 33 STING: Evaluation of Scientific and Technological Innovation and Progress Technology Institute, and7National Statistical Services of Greece, IT Division, Greece / chapter 34 The Semantic Conference Organizer