Advanced Practical Approaches to Web Mining Techniques and Application, Obaid Ahmed J., Polkowski Zdzislaw, Bhushan Bharat
Автор: Obaid Ahmed J., Polkowski Zdzislaw, Bhushan Bharat Название: Advanced Practical Approaches to Web Mining Techniques and Application ISBN: 1799894266 ISBN-13(EAN): 9781799894261 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 40887.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Illustrates all the concepts of web mining, from gathering the web data sources to discovering and representing the extracted knowledge. The book illustrates, analyses, and compares all the techniques, applications, and algorithms that are used in Web mining categories.
Описание: This textbook gives an in-depth discussion of basic principles and practical techniques of data mining and data warehousing. Theoretical concepts are discussed in detail with the help of practical examples. It covers data mining tools and language such as Weka and R language.
Описание: Like the popular second edition, Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining?including both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. <br><br>Complementing the book is a fully functional platform-independent open source Weka software for machine learning, available for free download. <br><br>The book is a major revision of the second edition that appeared in 2005. While the basic core remains the same, it has been updated to reflect the changes that have taken place over the last four or five years. The highlights for the updated new edition include completely revised technique sections; new chapter on Data Transformations, new chapter on Ensemble Learning, new chapter on Massive Data Sets, a new ?book release? version of the popular Weka machine learning open source software (developed by the authors and specific to the Third Edition); new material on ?multi-instance learning?; new information on ranking the classification, plus comprehensive updates and modernization throughout. All in all, approximately 100 pages of new material.<br> <br><br>* Thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques<br><br>* Algorithmic methods at the heart of successful data mining?including tired and true methods as well as leading edge methods<br><br>* Performance improvement techniques that work by transforming the input or output<br><br>* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization?in an updated, interactive interface. <br>
Описание: Presents the latest advances in business applications and the use of mining software solutions to achieve optimal decision-making and risk management results. Highlighting innovative studies on data warehousing, business activity monitoring, and text mining, this publication is an ideal reference source for research scholars, management faculty, and practitioners.
Описание: This book provides a general end-to-end discussion concerning the process of translating raw data to scientific and business decisions. The reader`s ability to find patterns in data will be greatly enhanced due to the book`s combination of statistical learning with powerful visualization techniques.
Описание: The main objective of ICSCTEA 2013 is to provide a platform for researchers, engineers and academicians from all over the world to present their research results and development activities in soft computing techniques and engineering application.
Описание: Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner(R), Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies.
Автор: Kumar A. V. Senthil Название: Web Usage Mining Techniques and Applications Across Industries ISBN: 1522506136 ISBN-13(EAN): 9781522506133 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 29106.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Web usage mining is defined as the application of data mining technologies to online usage patterns as a way to better understand and serve the needs of web-based applications. Because the internet has become a central component in information sharing and commerce, having the ability to analyze user behavior on the web has become a critical component to a variety of industries.Web Usage Mining Techniques and Applications Across Industries addresses the systems and methodologies that enable organizations to predict web user behavior as a way to support website design and personalization of web-based services and commerce. Featuring perspectives from a variety of sectors, this publication is designed for use by IT specialists, business professionals, researchers, and graduate-level students interested in learning more about the latest concepts related to web-based information retrieval and mining.
Описание: Are you using SAP ERP and eager to unlock the enormous value of its data? With this practical guide, SAP veterans Greg Foss and Paul Modderman show you how to use several data analysis tools to solve interesting problems with your SAP data.
Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.
Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research.
Please visit the book companion website at https: //www.cs.waikato.ac.nz/ ml/weka/book.html.
It contains
Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book
Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book
Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc.
Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects
Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods
Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface
Includes open-access online courses that introduce practical applications of the material in the book
Описание: Overcoming many challenges, data mining has already established discipline capability in many domains. This book discusses advances in modern data mining research in a global and technological environment.
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