Statistical Data Mining and Knowledge Discovery: 1st Edition (Hardback) book cover

Statistical Data Mining and Knowledge Discovery

1st Edition

Edited by Hamparsum Bozdogan

Chapman and Hall/CRC

624 pages | 175 B/W Illus.

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Hardback: 9781584883449
pub: 2003-07-29
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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.


"[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)

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

Subject Categories

BISAC Subject Codes/Headings:
COMPUTERS / Database Management / General
COMPUTERS / Database Management / Data Mining
MATHEMATICS / Probability & Statistics / General