Statistical Data Mining & 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.

Contents

• The Role of Bayesian and Frequentist Multivariate Modeling in Statistical Data Mining, S. James Press
• Intelligent Statistical Data Mining with Information Complexity and Genetic Algorithms, Hamparsum Bozdogan
• Econometric and Statistical Data Mining, Prediction and Policy-Making, Arnold Zellner
• Data Mining Strategies for the Detection of Chemical Warfare Agents, Jeffrey. L. Solka, Edward J. Wegman, and David J. Marchette
• Disclosure Limitation for Large Contingency Tables, Adrian Dobra, Elena A. Erosheva and Stephen E. Fienberg
• Partial Membership Models with Application to Disability Survey Data, Elena A. Erosheva
• Automated Scoring of Polygraph Data, Aleksandra B. Slavkovic
• Missing Value Algorithms in Decision Trees, Hyunjoong Kim and Sumer Yates
• Unsupervised Learning from Incomplete Data Using a Mixture Model Approach, Lynette Hunt and Murray Jorgensen
• Improving the Performance of Radial Basis Function (RBF), Zhenqiu Liu and Hamparsum Bozdogan
• Use of Kernel Based Techniques for Sensor Validation in Nuclear Power Plants, Andrei V. Gribok, Aleksey M. Urmanov, J. Wesley Hines, Robert E. Uhrig
• Data Mining and Traditional Regression, Christopher M. Hill, Linda C. Malone, and Linda Trocine
• An Extended Sliced Inverse Regression, Masahiro Mizuta Hokkaido University, Sapporo, Japan
• Using Genetic Programming to Improve the Group Method of Data Handling, M. Hiassat, M.F. Abbod, and N. Mort
• Data Mining for Monitoring Plant Devices Using GMDH and Pattern Classification, B.R. Upadhyaya and B. Lu
• Statistical Modeling and Data Mining to Identify the Consumer Preferences, Francois Boussu and Jean Jacques Denimal
• Testing for Structural Change Over Time of Brand Attribute Perceptions, Sara Dolnicar and Friedrich Leisch
• Kernel PCA for Feature Extraction with Information Complexity, Zhenqiu Liu and Hamparsum Bozdogan
• Global Principal Component Analysis for Dimensionality Reduction in Distributed Data Mining, Hairong Qi, Tsei-WeiWang, J. Douglas Birdwell
• A New Metric for Categorical Data, S. H. Al-Harbi, G. P. McKeown and V. J. Rayward-Smith
• Ordinal Logistic Modeling Using ICOMP as a Goodness-of-Fit Criterion, J. Michael Lanning and Hamparsum Bozdogan
• Comparing Latent Class Factor Analysis with the Traditional Approach in Data Mining, Jay Magidson and Jeroen Vermunt
• On Cluster Effects in Mining Complex Econometric Data, M. Ishaq Bhatti
• Neural Networks Based Data Mining Techniques For Steel Making, Ravindra K. Sarma, Amar Gupta, and Sanjeev Vadhavkar
• Solving Data Clustering Problem as a String Search Problem, V. Olman, D. Xu, and Y. Xu
• Behavior-Based Recommender Systems as Value-Added Services for Scientific Libraries, Andreas Geyer-Schulz, Michael Hahsler, Andreas Neumann, and Anke Thede
• GTP (General Text Parser) Software for Text Mining, Justin T. Giles, Ling Wo, Michael W. Berry
• Implication Intensity: From the Basic Statistical Definition to the Entropic Version, Julien Blanchard, Pascale Kuntz, Fabrice Guillet, Regis Gras
• Use of a Secondary Splitting Criterion in Classification Forest Construction, Chang-Yung Yu and Heping Zhang
• A Method Integrating Self-Organizing Maps to Predict the Probability of Barrier Removal, Zhicheng Zhang, and Frederic Vanderhaegen
• Cluster Analysis of Imputed Financial Data Using an Augmentation-Based Algorithm, H. Bensmail, R. P. DeGennaro
• Data Mining in Federal Agencies, David L. Banks and Robert T. Olszewski
• STING: Evaluation of Scientific & Technological Innovation and Progress, S. Sirmakessis, K. Markello, P. Markellou, G. Mayritsakis, K. Perdikouri, Tsakalidis, and Georgia Panagopoulou
• The Semantic Conference Organizer, Kevin Heinrich, Michael W. Berry, Jack J. Dongarra, Sathish Vadhiyar