Data Mining and Data Visualization

by Elsevier Science

Overview

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

Product Details

ISBN-13:	9780080459400
Publisher:	Elsevier Science
Publication date:	05/02/2005
Series:	ISSN , #24
Sold by:	Barnes & Noble
Format:	eBook
Pages:	800
File size:	23 MB
Note:	This product may take a few minutes to download.

About the Author

book “Ancient Inhabitants of Jebel Moya” published by the Cambridge Press under the joint authorship of Rao and two anthropologists. On the basis of work done at CU during the two year period, 1946-1948, Rao earned a Ph.D. degree and a few years later Sc.D. degree of CU and the rare honor of life fellowship of Kings College, Cambridge.

He retired from ISI in 1980 at the mandatory age of 60 after working for 40 years during which period he developed ISI as an international center for statistical education and research. He also took an active part in establishing state statistical bureaus to collect local statistics and transmitting them to Central Statistical Organization in New Delhi. Rao played a pivitol role in launching undergraduate and postgraduate courses at ISI. He is the author of 475 research publications and several breakthrough papers contributing to statistical theory and methodology for applications to problems in all areas of human endeavor. There are a number of classical statistical terms named after him, the most popular of which are Cramer-Rao inequality, Rao-Blackwellization, Rao’s Orthogonal arrays used in quality control, Rao’s score test, Rao’s Quadratic Entropy used in ecological work, Rao’s metric and distance which are incorporated in most statistical books.

He is the author of 10 books, of which two important books are, Linear Statistical Inference which is translated into German, Russian, Czec, Polish and Japanese languages,and Statistics and Truth which is translated into, French, German, Japanese, Mainland Chinese, Taiwan Chinese, Turkish and Korean languages.

He directed the research work of 50 students for the Ph.D. degrees who in turn produced 500 Ph.D.’s. Rao received 38 hon. Doctorate degree from universities in 19 countries spanning 6 continents. He received the highest awards in statistics in USA,UK and India: National Medal of Science awarded by the president of USA, Indian National Medal of Science awarded by the Prime Minister of India and the Guy Medal in Gold awarded by the Royal Statistical Society, UK. Rao was a recipient of the first batch of Bhatnagar awards in 1959 for mathematical sciences and and numerous medals in India and abroad from Science Academies. He is a Fellow of Royal Society (FRS),UK, and member of National Academy of Sciences, USA, Lithuania and Europe. In his honor a research Institute named as CRRAO ADVANCED INSTITUTE OF MATHEMATICS, STATISTICS AND COMPUTER SCIENCE was established in the campus of Hyderabad University.

Table of Contents

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.