Statistical Data Mining and Knowledge Discovery

Statistical Data Mining and Knowledge Discovery

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Features

  • Presents recent research and developments in statistical data mining
  • Reports on new models and innovative hybrid approaches useful in the analysis of large data sets
  • Identifies applications of data mining and knowledge discovery in areas such as market segmentation, image and speech analysis, and fraud detection
  • Features a chapter by Arnold Zellner on econometric and statistical data mining
  • Summary

    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.

    Table of 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 Methods Based on Bounds for Large Contingency Tables with Applications to Disability, 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) Classification Using Information Criteria, 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 in Time Series Prediction, 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 Consumer Preferences, Francois Boussu and Jean Jacques Denimal
    Testing for Structural Change Over Time of Brand Attribute Perceptions in Market Segments, 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-Wei Wang, 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

    Editorial Reviews

    "[N]icely produced … Overall, the book seems more worthwhile than many collections of edited papers arising from conference proceedings … ."
    - Journal of the Royal Statistical Society, Series A, Vol. 157 (3)

     
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