Data mining and data visualization
Author(s)
Bibliographic Information
Data mining and data visualization
（Handbook of statistics, v. 24）
Elsevier, 2005
1st ed
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Note
Includes bibliographical references and index
Description and Table of Contents
Description
Data Mining and Data Visualization 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.
Table of Contents
Chapter 1: Statistical Data Mining, Wegman, Edward J. and Solka, Jeffrey L.
Chapter 2: From Data Mining to Knowledge Mining, Kaufman, Kenneth A. and Michalski, Ryszard S.
Chapter 3: Mining Computer Security Data, Marchette, David J.
Chapter 4: Data Mining of Text Files, Martinez, Angel R.
Chapter 5: Text Data Mining with Minimal Spanning Trees, Solka, Jeffrey L., Bryant, Avory C., and Wegman, Edward J.
Chapter 6: Information Hiding: Steganography and Steganalysis, Duric, Zoran, Jacobs, Michael, and Jajodia, Sushil
Chapter 7: Canonical Variate Analysis and Related Methods for Reduction of Dimensionality and Graphical Representation, Rao, C. Radhakrishna
Chapter 8: Pattern Recognition, Hand, David J.
Chapter 9: Multivariate Density Estimation, Scott, David J. and Sain, Stephan R.
Chapter 10: Multivariate Outlier Detection and Robustness, Hubert, Mia, Rousseeuw, Peter J., and Van Aelst, Stefan
Chapter 11: Classification and Regression Trees, Bagging, 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.
by "Nielsen BookData"
