Data mining and data visualization
The editors of this special handbook believe in the following idea: the most innovative and fruitful methodological advances in statistics come when brilliant individuals have to cope with questions related to new data structures. The present volume is a collection of outstanding, data-centric papers, devoted to the attempt of data mining and data visualization to handle non-standard statistical data, i.e. , data that do not satisfy traditional assumptions of independence, stationarity, identical distribution, or parametric formulations. The book is divided into three main sections that involve 17 chapters, as follows: Section 1 is devoted to data mining (Chapters 1-6); Section 2 discusses statistical and related analytical methods applicable to data mining (Chapters 7-13); while Section 3 examines data visualization (Chapters 14-17). Authors and titles may provide a general, however closer idea on the highest quality papers enclosed in the present handbook: Chapter 1: Statistical Data Mining (Edward J. Wegman, Jeffrey L. Solka); Chapter 2: From Data Mining to Knowledge Mining (Kenneth A. Kaufman, Ryszard S. Michalski); Chapter 3: Mining Computer Security Data (David J. Marchette); Chapter 4: Data Mining of Text Files (Angel Martinez); Chapter 5: Text Data Mining with Minimal Spanning Trees (Jeffrey L. Solka, Avory C. Bryant, Edward J. Wegman); Chapter 6: Information Hiding: Steganography and Steganalysis (Zoran Duric, Michael Jacobs, Sushil Jajodia); Chapter 7: Canonical Variate Analysis and Related Methods for Reduction of Dimensionality and Graphical Representation (C. Radhakrishna Rao); Chapter 8: Patten Recognition (David J. Hand); Chapter 9: Multidimensional Density Estimation (David W. Scott, Stephan R. Sain); Chapter 10: Multivariate Outlier Detection and Robustness (Mia Hubert, Peter J. Rousseeuw, Stefan Van Aelst); Chapter 11: Classification and Regression Trees, Bagging, and Boosting (Clifton D. Sutton); Chapter 12: Fast Algorithms for Classification Using Class Cover Catch Digraphs (David J. Marchette, Edward J. Wegman, Carey E. Priebe); Chapter 13: On Genetic Algorithms and Their Applications (Yasmin H. Said); Chapter 14: Computational Methods for High-Dimensional Rotations in Data Visualization (Andreas Buja, Dianne Cook, Daniel Asimov, Catherine Hurley); Chapter 15: Some Recent Graphics Templates and Software for Showing Statistical Summaries (Daniel B. Carr); Chapter 16: Interactive Statistical Graphics: the Paradigm of Linked Views (Adalbert Wilhelm); Chapter 17: Data Visualization and Virtual Reality (Jim X. Chen).
scientific article; zbMATH DE number 2174306