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Other titles in the Handbook of Statistics series:
Handbook of Statistics: Data Mining and Data Visualizationby C. R. Rao
Synopses & Reviews
This book focuses on dealing with large-scale data, a field commonly referred to as data mining. The book is divided into three sections. The first deals with an introduction to statistical aspects of data mining and machine learning and includes applications to text analysis, computer intrusion detection, and hiding of information in digital files. The second section focuses on a variety of statistical methodologies that have proven to be effective in data mining applications. These include clustering, classification, multivariate density estimation, tree-based methods, pattern recognition, outlier detection, genetic algorithms, and dimensionality reduction. The third section focuses on data visualization and covers issues of visualization of high-dimensional data, novel graphical techniques with a focus on human factors, interactive graphics, and data visualization using virtual reality. This book represents a thorough cross section of internationally renowned thinkers who are inventing methods for dealing with a new data paradigm.
- Distinguished contributors who are international experts in aspects of data mining
- Includes data mining approaches to non-numerical data mining including text data, Internet traffic data, and geographic data
- Highly topical discussions reflecting current thinking on contemporary technical issues, e.g. streaming data
- Discusses taxonomy of dataset sizes, computational complexity, and scalability usually ignored in most discussions
- Thorough discussion of data visualization issues blending statistical, human factors, and computational insights
· Distinguished contributors who are international experts in aspects of data mining
· Includes data mining approaches to non-numerical data mining including text data, Internet traffic data, and geographic data
· Highly topical discussions reflecting current thinking on contemporary technical issues, e.g. streaming data
· Discusses taxonomy of dataset sizes, computational complexity, and scalability usually ignored in most discussions
· Thorough discussion of data visualization issues blending statistical, human factors, and computational insights
About the Author
C. R. Rao, born in India is one of this century's foremost statisticians, received his education in statistics at the Indian Statistical Institute (ISI), Calcutta. Rao is currently at Penn State as Eberly Professor of Statistics and Director of the Center for Multivariate Analysis. His research has influenced not only statistics, but also the physical, social and natural sciences and engineering.
The Pennsylvania State University, PA, USA
Table of Contents
agging, and Boosting, Sutton, Clifton D.
Chapter 12: Fast Algorithms for Classification Using Class Cover Catch Digraphs, Marchette, David J., Wegman, Edward J., and Priebe, Carey E.
Chapter 13: On Genetic Algorithms and their Applications, Said, Yasmin
Chapter 14: Computational Methods for High-Dimensional Rotations in Data Visualization, Buja, Andreas, Cook, Dianne, Asimov, Daniel, and Hurley, Catherine
Chapter 15: Some Recent Graphics Templates and Software for Showing Statistical Summaries, Carr, Daniel B.
Chapter 16: Interactive Statistical Graphics: The Paradigm of Linked Views, Wilhelm, Adalbert
Chapter 17: Data Visualization and Virtual Reality, Chen, Jim X.
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