Statistical Data Mining and Knowledge Discovery

Massive data sets pose a great challenge to many cross-disciplinary fields, including statistics. The high dimensionality and different data types and structures have now outstripped the capabilities of traditional statistical, graphical, and data visualization tools. Extracting useful information from such large data sets calls for novel approaches that meld concepts, tools, and techniques from diverse areas, such as computer science, statistics, artificial intelligence, and financial engineering. Statistical Data Mining and Knowledge Discovery brings together a stellar panel of experts to discuss and disseminate recent developments in data analysis techniques for data mining and knowledge extraction. This carefully edited collection provides a practical, multidisciplinary perspective on using statistical techniques in areas such as market segmentation, customer profiling, image and speech analysis, and fraud detection. The chapter authors, who include such luminaries as Arnold Zellner, S. James Press, Stephen Fienberg, and Edward K. Wegman, present novel approaches and innovative models and relate their experiences in using data mining techniques in a wide range of applications.

FOREWORD -- EDITOR'S PREFACE -- EDITOR'S BIO -- LIST OF PRESENTERS -- Contents -- 1 The Role of Bayesian and Frequentist Multivariate Modeling in Statistical Data Mining -- 2 Intelligent Statistical Data Mining with Information Complexity and Genetic Algorithms -- 3 Econometric and Statistical Data Mining, Prediction and Policy-Making -- 4 Data Mining Strategies for the Detection of Chemical Warfare Agents -- 5 Disclosure Limitation Methods Based on Bounds for Large Contingency Tables With Applications to Disability -- 6 Partial Membership Models with Application to Disability Survey Data -- 7 Automated Scoring of Polygraph Data -- 8 Missing Value Algorithms in Decision Trees -- 9 Unsupervised Learning from Incomplete Data Using a Mixture Model Approach -- 10 Improving the Performance of Radial Basis Function (RBF) Classification Using Information Criteria -- 11 Use of Kernel Based Techniques for Sensor Validation in Nuclear Power Plants -- 12 Data Mining and Traditional Regression -- 13 An Extended Sliced Inverse Regression -- 14 Using Genetic Programming to Improve the Group Method of Data Handling in Time Series Prediction -- 15 Data Mining for Monitoring Plant Devices Using GM…