Описание: Churn prediction, recognition, and mitigation have become essential topics in various industries. As a means for forecasting and manageing risk, further research in this field can greatly assist companies in making informed decisions based on future possible scenarios.Developing Churn Models Using Data Mining Techniques and Social Network Analysis provides an in-depth analysis of attrition modeling relevant to business planning and management. Through its insightful and detailed explanation of best practices, tools, and theory surrounding churn prediction and the integration of analytics tools, this publication is especially relevant to managers, data specialists, business analysts, academicians, and upper-level students.
Описание: Biologically-inspired data mining has a wide variety of applications in areas such as data clustering, classification, sequential pattern mining, and information extraction in healthcare and bioinformatics. Over the past decade, research materials in this area have dramatically increased, providing clear evidence of the popularity of these techniques.Biologically-Inspired Techniques for Knowledge Discovery and Data Mining exemplifies prestigious research and shares the practices that have allowed these areas to grow and flourish. This essential reference publication highlights contemporary findings in the area of biologically-inspired techniques in data mining domains and their implementation in real-life problems. Providing quality work from established researchers, this publication serves to extend existing knowledge within the research communities of data mining and knowledge discovery, as well as for academicians and students in the field.
Автор: Jiawei Han Название: Data Mining: Concepts and Techniques, ISBN: 0123814790 ISBN-13(EAN): 9780123814791 Издательство: Elsevier Science Рейтинг: Цена: 10645 р. Наличие на складе: Нет в наличии.
Описание: Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining.
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>
Автор: Takashi Washio; Einoshin Suzuki; Kai Ming Ting; Ak Название: Advances in Knowledge Discovery and Data Mining ISBN: 3540681248 ISBN-13(EAN): 9783540681243 Издательство: Springer Рейтинг: Цена: 24489 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 12th Pacific-Asia Conference PAKDD 2008 Osaka Japan May 20-23 2008 Proceedings.
Автор: Tsau Young Lin; Setsuo Ohsuga; Churn-Jung Liau; Xi Название: Foundations of Data Mining and Knowledge Discovery ISBN: 364243228X ISBN-13(EAN): 9783642432286 Издательство: Springer Рейтинг: Цена: 29846 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: "Foundations of Data Mining and Knowledge Discovery" contains the latest results and new directions in data mining research.
Автор: Chu Wesley W. Название: Data Mining and Knowledge Discovery for Big Data ISBN: 3642408362 ISBN-13(EAN): 9783642408366 Издательство: Springer Рейтинг: Цена: 22886 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book address topics ranging from mining data from opinion, spatiotemporal databases, discriminative subgraph patterns, path knowledge discovery, social media, and privacy issues to the subject of computation reduction via binary matrix factorization.
Автор: Guangren Shi Название: Data Mining and Knowledge Discovery for Geoscientists ISBN: 0124104371 ISBN-13(EAN): 9780124104372 Издательство: Elsevier Science Рейтинг: Цена: 16422 р. Наличие на складе: Нет в наличии.
Описание: Addresses challenges by summarizing the developments in geosciences data mining and arming scientists with the ability to apply key concepts to effectively analyze and interpret vast amounts of critical information. This title focuses on 22 of data mining`s practical algorithms and application samples.
Описание: Solutions Manual to accompany Statistical Data Analytics: Foundations for Data Mining, Informatics, and Knowledge Discovery A comprehensive introduction to statistical methods for data mining and knowledge discovery.
Описание: This book constitutes the thoroughly refereed post-workshop proceedings at PAKDD Workshops 2016, held in conjunction with PAKDD, the 20th Pacific-Asia Conference on Knowledge Discovery and Data Mining in Auckland, New Zealand, in April 2016. The 23 revised papers presented were carefully reviewed and selected from 38 submissions.
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
Автор: Il-Yeol Song; Johann Eder; Tho Manh Nguyen Название: Data Warehousing and Knowledge Discovery ISBN: 3540858350 ISBN-13(EAN): 9783540858355 Издательство: Springer Рейтинг: Цена: 13315 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Includes the refereed proceedings of the 10th International Conference on Data Warehousing and Knowledge Discovery, DaWak 2008, held in Turin, Italy, in September 2008. This book contains 40 papers that were selected from 143 submissions.
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