Data mining and data visualization
type: Item
instance of: scholarly article
title: Data mining and data visualization
zbMATH Open document ID: 1093.62007
published in: Handbook of Statistics
publication date: 2005-06-07T00:00:00Z
review text: 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 Usin…
reviewed by: Neculai Curteanu
Mathematics Subject Classification ID: 62-07
Mathematics Subject Classification ID: 62A09
Mathematics Subject Classification ID: 62-00
Mathematics Subject Classification ID: 62-06
Mathematics Subject Classification ID: 00B15
zbMATH DE Number: 2174306
zbMATH Keywords: handbook of statistics
zbMATH Keywords: data mining
zbMATH Keywords: knowledge mining
zbMATH Keywords: non-standard statistical data
zbMATH Keywords: data visualization
zbMATH Keywords: new data structures
zbMATH Keywords: minimal spanning trees
zbMATH Keywords: multidimensional density estimation
zbMATH Keywords: multivariate outlier detection
zbMATH Keywords: classification algorithms
MaRDI profile type: Publication
description: scientific article; zbMATH DE number 2174306