Handbook of Research on Advanced Data Mining Techniques and Applications for Business Intelligence, Shrawan Kumar Trivedi, Shubhamoy Dey, Anil Kumar, Tapan Kumar Panda
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
As data holdings get bigger and questions get harder, data scientists and analysts must focus on the systems, the tools and techniques, and the disciplined process to get the correct answer, quickly Whether you work within industry or government, this book will provide you with a foundation to successfully and confidently process large amounts of quantitative data.
Here are just a dozen of the many questions answered within these pages:
What does quantitative analysis of a system really mean?
What is a system?
What are big data and analystics?
How do you know your numbers are good?
What will the future data science environment look like?
How do you determine data provenance?
How do you gather and process information, and then organize, store, and synthesize it?
How does an organization implement data analytics?
Do you really need to think like a Chief Information Officer?
What is the best way to protect data?
What makes a good dashboard?
What is the relationship between eating ice cream and getting attacked by a shark?
The nine chapters in this book are arranged in three parts that address systems concepts in general, tools and techniques, and future trend topics. Systems concepts include contrasting open and closed systems, performing data mining and big data analysis, and gauging data quality. Tools and techniques include analyzing both continuous and discrete data, applying probability basics, and practicing quantitative analysis such as descriptive and inferential statistics. Future trends include leveraging the Internet of Everything, modeling Artificial Intelligence, and establishing a Data Analytics Support Office (DASO).
Many examples are included that were generated using common software, such as Excel, Minitab, Tableau, SAS, and Crystal Ball. While words are good, examples can sometimes be a better teaching tool. For each example included, data files can be found on the companion website. Many of the data sets are tied to the global economy because they use data from shipping ports, air freight hubs, largest cities, and soccer teams. The appendices contain more detailed analysis including the 10 T's for Data Mining, Million Row Data Audit (MRDA) Processes, Analysis of Rainfall, and Simulation Models for Evaluating Traffic Flow.
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.
Описание: Data mining is an important branch of computer science and information technology management that deals with the discovery and analysis of datasets. This book covers in detail some existent theories as well as innovative concepts revolving around data mining such as bio data analytics, analysis of social structures and patterns, correlations and fluctuations, etc. With its detailed analyses and data, this book will prove immensely beneficial to professionals and students involved in this area at various levels.
Автор: Ronghuai Huang; Qiang Yang; Jian Pei; Jo?o Gama; X Название: Advanced Data Mining and Applications ISBN: 3642033474 ISBN-13(EAN): 9783642033476 Издательство: Springer Рейтинг: Цена: 19564.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 5th International Conference ADMA 2009 Chengdu China August 1719 2009 Proceedings. .
Автор: Tang Название: Advanced Data Mining and Applications ISBN: 3540881913 ISBN-13(EAN): 9783540881919 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Constitutes the refereed proceedings of the 4th International Conference on Advanced Data Mining and Applications, ADMA 2008, held in Chengdu, China, in October 2008. This book focuses on advancements in data mining and peculiarities and challenges of real world applications using data mining.
Автор: Bhattacharyya Siddhartha, Banerjee Pinaki, Majumdar Dipankar Название: Handbook of Research on Advanced Hybrid Intelligent Techniques and Applications ISBN: 1466694742 ISBN-13(EAN): 9781466694743 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 41580.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Conventional computational methods, and even the latest soft computing paradigms, often fall short in their ability to offer solutions to many real-world problems due to uncertainty, imprecision, and circumstantial data. Hybrid intelligent computing is a paradigm that addresses these issues to a considerable extent.The Handbook of Research on Advanced Research on Hybrid Intelligent Techniques and Applications highlights the latest research on various issues relating to the hybridization of artificial intelligence, practical applications, and best methods for implementation. Focusing on key interdisciplinary computational intelligence research dealing with soft computing techniques, pattern mining, data analysis, and computer vision, this book is relevant to the research needs of academics, IT specialists, and graduate-level students.
Описание: Big data is a well-trafficked subject in recent IT discourse and does not lack for current research. In fact, there is such a surfeit of material related to big data—and so much of it of questionably reliability, thanks to the high-gloss efforts of savvy tech-marketing gurus—that it can, at times, be difficult for a serious academician to navigate.The Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence cuts through the haze of glitz and pomp surrounding big data and offers a simple, straightforward reference-source of practical academic utility. Covering such topics as cloud computing, parallel computing, natural language processing, and personalized medicine, this volume presents an overview of current research, insight into recent advances, and gaps in the literature indicative of opportunities for future inquiry and is targeted toward a broad, interdisciplinary audience of students, academics, researchers, and professionals in fields of IT, networking, and data-analytics.
Автор: Yang Название: Optimization Techniques and Applications with Examples ISBN: 1119490545 ISBN-13(EAN): 9781119490548 Издательство: Wiley Рейтинг: Цена: 16466.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
A guide to modern optimization applications and techniques in newly emerging areas spanning optimization, data science, machine intelligence, engineering, and computer sciences
Optimization Techniques and Applications with Examples introduces the fundamentals of all the commonly used techniques in optimization that encompass the broadness and diversity of the methods (traditional and new) and algorithms. The author--a noted expert in the field--covers a wide range of topics including mathematical foundations, optimization formulation, optimality conditions, algorithmic complexity, linear programming, convex optimization, and integer programming. In addition, the book discusses artificial neural network, clustering and classifications, constraint-handling, queueing theory, support vector machine and multi-objective optimization, evolutionary computation, nature-inspired algorithms and many other topics.
Designed as a practical resource, all topics are explained in detail with step-by-step examples to show how each method works. The book's exercises test the acquired knowledge that can be potentially applied to real problem solving. By taking an informal approach to the subject, the author helps readers to rapidly acquire the basic knowledge in optimization, operational research, and applied data mining. This important resource:
Offers an accessible and state-of-the-art introduction to the main optimization techniques
Contains both traditional optimization techniques and the most current algorithms and swarm intelligence-based techniques
Presents a balance of theory, algorithms, and implementation
Includes more than 100 worked examples with step-by-step explanations
Written for upper undergraduates and graduates in a standard course on optimization, operations research and data mining, Optimization Techniques and Applications with Examples is a highly accessible guide to understanding the fundamentals of all the commonly used techniques in optimization.
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: 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>
Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application.
This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas--from science and engineering, to medicine, academia and commerce.
Includes input by practitioners for practitioners
Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models
Contains practical advice from successful real-world implementations
Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions
Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications
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