Data Mining and Data VisualizationData Mining and Data Visualization focuses on dealing with largescale 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, treebased methods, pattern recognition, outlier detection, genetic algorithms, and dimensionality reduction. The third section focuses on data visualization and covers issues of visualization of highdimensional 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.

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Contents
1  
47  
3 Mining Computer Securitycomputer security Data  77 
4 Data Mining of Text Files  109 
5 Text Data Mining with Minimal Spanning Trees  133 
Steganography and Steganalysis  171 
7 Canonical Variate Analysis and Related Methods for Reduction of Dimensionality and Graphical Representation  189 
8 Pattern Recognition  213 
12 Fast Algorithms for Classification Using Class Cover Catch Digraphs  331 
13 On Genetic Algorithms and their Applications  359 
14 Computational Methods for HighDimensional Rotations in Data Visualization  391 
15 Some Recent Graphics Templates and Software for Showing Statistical Summaries  415 
the Paradigm of Linked Views  437 
17 Data Visualization and Virtual Reality  539 
back matter  565 
609  
9 Multidimensional Density Estimation  229 
10 Multivariate Outlier Detection and Robustness  263 
11 Classification and Regression Trees Bagging and Boosting  303 
Contents of Previous Volumes  619 
Other editions  View all
Data Mining and Data Visualization Calyampudi Radhakrishna Rao,Edward J. Wegman,Jeffrey L. Solka No preview available  2005 
Common terms and phrases
applications approach attributes bar chart bits boxplots C.R. Rao canonical coordinates classifier clustering color figures section color reproduction corresponding covariance crossover data analysis data mining data set data visualization database defined density estimation detection dimensions distance distribution dominating set example exploratory data analysis frame function genetic algorithms graph graphical elements groups Hellinger distance highlighting histogram IEEE implemented interactive statistical kernel knowledge mining learning sample linear linking LM plots Machine Learning matrix measure methods Michalski misclassification rate mosaic plot multivariate nodes nonparametric objects observations operator optimal Order Statistics outliers packets parallel coordinates parameters pattern pixels prediction principal components problem profiles projection random regression represent robust rotations rule sample population scatterplot selection sequence shows space split statistical graphics steganalysis steganography strings structure subset techniques tion tree values variables vector Wegman
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