Data Mining and Data VisualizationElsevier, 2005. gada 2. maijs - 800 lappuses 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.
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Saturs
| 1 | |
| 47 | |
| 77 | |
| 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 |
index | 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 |
Citi izdevumi - Skatīt visu
Data Mining and Data Visualization Calyampudi Radhakrishna Rao,Edward J. Wegman,Jeffrey L. Solka Priekšskatījums nav pieejams - 2005 |
Bieži izmantoti vārdi un frāzes
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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169. lappuse - The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the US Government.
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