Data Mining and Data Visualization

 
 
Elsevier Science & Techn. (Verlag)
  • erschienen am 2. Mai 2005
  • |
  • 800 Seiten
 
E-Book | ePUB mit Adobe-DRM | Systemvoraussetzungen
978-0-08-045940-0 (ISBN)
 
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.
  • 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
  • Englisch
  • Amsterdam
  • |
  • Niederlande
  • 20,72 MB
978-0-08-045940-0 (9780080459400)
weitere Ausgaben werden ermittelt
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.

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