Data Mining X
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Data Mining X
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Author : A. Zanasi
language : en
Publisher: WIT Press
Release Date : 2009
Data Mining X written by A. Zanasi and has been published by WIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Computers categories.
Since the end of the Cold War, the threat of large-scale wars has been substituted by new threats: terrorism, organised crime, trafficking, smuggling, proliferation of weapons of mass destruction. To react to them, a security strategy is necessary, but in order to be effective it requires several instruments, including technological tools. Consequently, research and development in the field of security is proving to be an ever-expanding field all over the world. Data mining is seen more and more not only as a key technology in business, engineering and science but as one of the key features in security. To stress that all these technologies must be seen as a way to improve not only the security of citizens but also their freedom, special attention will be given to data protection research issues. The 10th International Conference on Data Mining is part of the successful series and the topics include: Text mining and text analytics; Data mining applications; Data mining methods.
Data Mining Southeast Asia Edition
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Author : Jiawei Han
language : en
Publisher: Elsevier
Release Date : 2006-04-06
Data Mining Southeast Asia Edition written by Jiawei Han and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-04-06 with Computers categories.
Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge. Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data— including stream data, sequence data, graph structured data, social network data, and multi-relational data. - A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data - Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning - Dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects - Complete classroom support for instructors at www.mkp.com/datamining2e companion site
Database Security X
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Author : Pierangela Samarati
language : en
Publisher: Springer
Release Date : 2016-01-09
Database Security X written by Pierangela Samarati and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-01-09 with Computers categories.
The purpose of this book is to discuss, in depth, the current state of research and practice in database security, to enable readers to expand their knowledge. The book brings together contributions from experts in the field throughout the world. Database security is still a key topic in mist businesses and in the public sector, having implications for the whole of society.
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Author :
language : en
Publisher: IOS Press
Release Date :
written by and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Data Mining Ix
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Author : A. Zanasi
language : en
Publisher: WIT Press
Release Date : 2008
Data Mining Ix written by A. Zanasi and has been published by WIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Computers categories.
Bringing together papers presented at the ninth International Conference on Data Mining, this book addresses the developments in this important field. Featured topics include: data preparation, clustering technologies, customer relationship management, text mining, web mining, and categorisation methods.
Data Mining In Agriculture
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Author : Antonio Mucherino
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-08-19
Data Mining In Agriculture written by Antonio Mucherino and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-08-19 with Mathematics categories.
Data Mining in Agriculture represents a comprehensive effort to provide graduate students and researchers with an analytical text on data mining techniques applied to agriculture and environmental related fields. This book presents both theoretical and practical insights with a focus on presenting the context of each data mining technique rather intuitively with ample concrete examples represented graphically and with algorithms written in MATLAB®.
Data Mining
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Author : Krzysztof J. Cios
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-10-05
Data Mining written by Krzysztof J. Cios and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-10-05 with Computers categories.
“If you torture the data long enough, Nature will confess,” said 1991 Nobel-winning economist Ronald Coase. The statement is still true. However, achieving this lofty goal is not easy. First, “long enough” may, in practice, be “too long” in many applications and thus unacceptable. Second, to get “confession” from large data sets one needs to use state-of-the-art “torturing” tools. Third, Nature is very stubborn — not yielding easily or unwilling to reveal its secrets at all. Fortunately, while being aware of the above facts, the reader (a data miner) will find several efficient data mining tools described in this excellent book. The book discusses various issues connecting the whole spectrum of approaches, methods, techniques and algorithms falling under the umbrella of data mining. It starts with data understanding and preprocessing, then goes through a set of methods for supervised and unsupervised learning, and concludes with model assessment, data security and privacy issues. It is this specific approach of using the knowledge discovery process that makes this book a rare one indeed, and thus an indispensable addition to many other books on data mining. To be more precise, this is a book on knowledge discovery from data. As for the data sets, the easy-to-make statement is that there is no part of modern human activity left untouched by both the need and the desire to collect data. The consequence of such a state of affairs is obvious.
Data Mining And Data Visualization
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Author :
language : en
Publisher: Elsevier
Release Date : 2005-05-02
Data Mining And Data Visualization written by and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-05-02 with Mathematics categories.
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
Data Mining And Business Analytics With R
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Author : Johannes Ledolter
language : en
Publisher: John Wiley & Sons
Release Date : 2013-05-28
Data Mining And Business Analytics With R written by Johannes Ledolter and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-05-28 with Mathematics categories.
Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification. Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents: A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools Illustrations of how to use the outlined concepts in real-world situations Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials Numerous exercises to help readers with computing skills and deepen their understanding of the material Data Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.
Advanced Data Mining And Applications
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Author : Xue Li
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-07-26
Advanced Data Mining And Applications written by Xue Li and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-07-26 with Computers categories.
Here are the proceedings of the 2nd International Conference on Advanced Data Mining and Applications, ADMA 2006, held in Xi'an, China, August 2006. The book presents 41 revised full papers and 74 revised short papers together with 4 invited papers. The papers are organized in topical sections on association rules, classification, clustering, novel algorithms, multimedia mining, sequential data mining and time series mining, web mining, biomedical mining, advanced applications, and more.